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<h2 id="toc-title">Table of contents</h2>
<ul>
<li><a href="#introducao" id="toc-introducao" class="nav-link active" data-scroll-target="#introducao"><span class="header-section-number">1</span> Introducao</a></li>
<li><a href="#preparacao-do-ambiente" id="toc-preparacao-do-ambiente" class="nav-link" data-scroll-target="#preparacao-do-ambiente"><span class="header-section-number">2</span> Preparacao do ambiente</a></li>
<li><a href="#caminho-do-dataset" id="toc-caminho-do-dataset" class="nav-link" data-scroll-target="#caminho-do-dataset"><span class="header-section-number">3</span> Caminho do dataset</a></li>
<li><a href="#leitura-dos-arquivos-de-imagem" id="toc-leitura-dos-arquivos-de-imagem" class="nav-link" data-scroll-target="#leitura-dos-arquivos-de-imagem"><span class="header-section-number">4</span> Leitura dos arquivos de imagem</a></li>
<li><a href="#amostra-completa-sem-limite-por-classe" id="toc-amostra-completa-sem-limite-por-classe" class="nav-link" data-scroll-target="#amostra-completa-sem-limite-por-classe"><span class="header-section-number">5</span> Amostra completa (sem limite por classe)</a></li>
<li><a href="#modelo-pre-treinado-transfer-learning" id="toc-modelo-pre-treinado-transfer-learning" class="nav-link" data-scroll-target="#modelo-pre-treinado-transfer-learning"><span class="header-section-number">6</span> Modelo pre-treinado (Transfer Learning)</a></li>
<li><a href="#extracao-de-atributos-por-transfer-learning" id="toc-extracao-de-atributos-por-transfer-learning" class="nav-link" data-scroll-target="#extracao-de-atributos-por-transfer-learning"><span class="header-section-number">7</span> Extracao de atributos por Transfer Learning</a></li>
<li><a href="#separacao-entre-treino-e-teste" id="toc-separacao-entre-treino-e-teste" class="nav-link" data-scroll-target="#separacao-entre-treino-e-teste"><span class="header-section-number">8</span> Separacao entre treino e teste</a></li>
<li><a href="#pre-processamento" id="toc-pre-processamento" class="nav-link" data-scroll-target="#pre-processamento"><span class="header-section-number">9</span> Pre-processamento</a></li>
<li><a href="#modelo-1-svm-linear" id="toc-modelo-1-svm-linear" class="nav-link" data-scroll-target="#modelo-1-svm-linear"><span class="header-section-number">10</span> Modelo 1: SVM linear</a></li>
<li><a href="#modelo-2-svm-radial" id="toc-modelo-2-svm-radial" class="nav-link" data-scroll-target="#modelo-2-svm-radial"><span class="header-section-number">11</span> Modelo 2: SVM radial</a></li>
<li><a href="#modelo-3-ajuste-de-hiperparametros-do-svm-radial" id="toc-modelo-3-ajuste-de-hiperparametros-do-svm-radial" class="nav-link" data-scroll-target="#modelo-3-ajuste-de-hiperparametros-do-svm-radial"><span class="header-section-number">12</span> Modelo 3: Ajuste de hiperparametros do SVM radial</a></li>
<li><a href="#modelo-4-pca-svm-radial" id="toc-modelo-4-pca-svm-radial" class="nav-link" data-scroll-target="#modelo-4-pca-svm-radial"><span class="header-section-number">13</span> Modelo 4: PCA + SVM radial</a></li>
<li><a href="#modelo-5-random-forest" id="toc-modelo-5-random-forest" class="nav-link" data-scroll-target="#modelo-5-random-forest"><span class="header-section-number">14</span> Modelo 5: Random Forest</a></li>
<li><a href="#comparacao-dos-modelos" id="toc-comparacao-dos-modelos" class="nav-link" data-scroll-target="#comparacao-dos-modelos"><span class="header-section-number">15</span> Comparacao dos modelos</a></li>
<li><a href="#importancia-das-variaveis" id="toc-importancia-das-variaveis" class="nav-link" data-scroll-target="#importancia-das-variaveis"><span class="header-section-number">16</span> Importancia das variaveis</a></li>
<li><a href="#salvamento-dos-resultados" id="toc-salvamento-dos-resultados" class="nav-link" data-scroll-target="#salvamento-dos-resultados"><span class="header-section-number">17</span> Salvamento dos resultados</a></li>
<li><a href="#validacao-cruzada-k-fold" id="toc-validacao-cruzada-k-fold" class="nav-link" data-scroll-target="#validacao-cruzada-k-fold"><span class="header-section-number">18</span> Validacao Cruzada K-Fold</a></li>
<li><a href="#comparacao-holdout-vs-k-fold" id="toc-comparacao-holdout-vs-k-fold" class="nav-link" data-scroll-target="#comparacao-holdout-vs-k-fold"><span class="header-section-number">19</span> Comparacao: Holdout vs K-Fold</a></li>
<li><a href="#conclusao" id="toc-conclusao" class="nav-link" data-scroll-target="#conclusao"><span class="header-section-number">20</span> Conclusao</a></li>
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<h1 class="title">Classificacao de Imagens RGB com SVM (Dataset Completo)</h1>
<p class="subtitle lead">CEAO-802 - Metodos de Analise de Dados</p>
</div>
<div class="quarto-title-meta">
<div>
<div class="quarto-title-meta-heading">Authors</div>
<div class="quarto-title-meta-contents">
<p>1T Generoso </p>
<p>1T João Marcos </p>
<p>1T Vitor Cesa </p>
</div>
</div>
</div>
</header>
<section id="introducao" class="level1" data-number="1">
<h1 data-number="1"><span class="header-section-number">1</span> Introducao</h1>
<p>Este trabalho aplica tecnicas de classificacao supervisionada ao dataset RGB disponibilizado na disciplina CEAO-802 - Metodos de Analise de Dados.</p>
<p>O documento de datasets apresenta um problema de classificacao de cenas urbanas com imagens RGB e TIR coletadas por drone sobre Guaratingueta (SP), com 13 classes de cobertura urbana. Nesta analise sera utilizada apenas a parte RGB do dataset, armazenada localmente em <code>D:/Vitor/Documents/CEAO/802/HAB/DADOS CEAO 2026/RGB</code>.</p>
<p><strong>Diferenca em relacao ao trabalho original:</strong> esta versao utiliza TODAS as imagens disponiveis por classe (sem limite de 50), para avaliar o impacto do tamanho do dataset no desempenho dos modelos.</p>
<p>A abordagem adotada segue a sugestao do material da disciplina: em vez de extrair atributos estatisticos simples (media, desvio, histogramas), serao utilizadas <strong>redes neurais convolucionais pre-treinadas</strong> como extratores de atributos (<em>transfer learning</em>). A rede MobileNetV2, pre-treinada no ImageNet, e carregada sem a camada de classificacao final. Cada imagem e redimensionada para <code>224x224</code> pixels e processada pela rede, que produz um vetor de 1280 atributos representando caracteristicas visuais de alto nivel aprendidas em milhoes de imagens. Esses vetores sao entao usados como entrada para os classificadores SVM e Random Forest.</p>
<p>Neste relatorio, serao avaliados:</p>
<ol type="1">
<li>SVM com kernel linear;</li>
<li>SVM com kernel radial;</li>
<li>SVM radial com ajuste simples de hiperparametros;</li>
<li>PCA + SVM radial;</li>
<li>Random Forest, como modelo de comparacao.</li>
</ol>
</section>
<section id="preparacao-do-ambiente" class="level1" data-number="2">
<h1 data-number="2"><span class="header-section-number">2</span> Preparacao do ambiente</h1>
<div class="cell">
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb1"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="co"># ============================================================</span></span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="co"># 1. Instalacao e carregamento dos pacotes</span></span>
<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="co"># ============================================================</span></span>
<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a>pacotes <span class="ot">&lt;-</span> <span class="fu">c</span>(</span>
<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a> <span class="st">"tidyverse"</span>,</span>
<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a> <span class="st">"keras3"</span>,</span>
<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a> <span class="st">"caret"</span>,</span>
<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a> <span class="st">"e1071"</span>,</span>
<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a> <span class="st">"randomForest"</span>,</span>
<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a> <span class="st">"knitr"</span></span>
<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a>pacotes_faltando <span class="ot">&lt;-</span> <span class="fu">setdiff</span>(pacotes, <span class="fu">rownames</span>(<span class="fu">installed.packages</span>()))</span>
<span id="cb1-15"><a href="#cb1-15" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-16"><a href="#cb1-16" aria-hidden="true" tabindex="-1"></a><span class="cf">if</span> (<span class="fu">length</span>(pacotes_faltando) <span class="sc">&gt;</span> <span class="dv">0</span>) {</span>
<span id="cb1-17"><a href="#cb1-17" aria-hidden="true" tabindex="-1"></a> <span class="fu">install.packages</span>(pacotes_faltando)</span>
<span id="cb1-18"><a href="#cb1-18" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb1-19"><a href="#cb1-19" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-20"><a href="#cb1-20" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(tidyverse)</span>
<span id="cb1-21"><a href="#cb1-21" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(keras3)</span>
<span id="cb1-22"><a href="#cb1-22" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(caret)</span>
<span id="cb1-23"><a href="#cb1-23" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(e1071)</span>
<span id="cb1-24"><a href="#cb1-24" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(randomForest)</span>
<span id="cb1-25"><a href="#cb1-25" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(knitr)</span>
<span id="cb1-26"><a href="#cb1-26" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-27"><a href="#cb1-27" aria-hidden="true" tabindex="-1"></a>backend_ativo <span class="ot">&lt;-</span> <span class="fu">tryCatch</span>(<span class="fu">config_backend</span>(), <span class="at">error =</span> <span class="cf">function</span>(e) <span class="cn">NA_character_</span>)</span>
<span id="cb1-28"><a href="#cb1-28" aria-hidden="true" tabindex="-1"></a><span class="fu">cat</span>(<span class="st">"Backend keras3:"</span>, backend_ativo, <span class="st">"</span><span class="sc">\n</span><span class="st">"</span>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="cell-output cell-output-stdout">
<pre><code>Backend keras3: tensorflow </code></pre>
</div>
</div>
</section>
<section id="caminho-do-dataset" class="level1" data-number="3">
<h1 data-number="3"><span class="header-section-number">3</span> Caminho do dataset</h1>
<div class="cell">
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb3"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="co"># ============================================================</span></span>
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a><span class="co"># 2. Caminho do dataset RGB</span></span>
<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a><span class="co"># ============================================================</span></span>
<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a>caminho_rgb <span class="ot">&lt;-</span> <span class="st">"D:/Vitor/Documents/CEAO/802/HAB/DADOS CEAO 2026/RGB"</span></span>
<span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb3-7"><a href="#cb3-7" aria-hidden="true" tabindex="-1"></a><span class="cf">if</span> (<span class="sc">!</span><span class="fu">dir.exists</span>(caminho_rgb)) {</span>
<span id="cb3-8"><a href="#cb3-8" aria-hidden="true" tabindex="-1"></a> <span class="fu">stop</span>(<span class="st">"A pasta do dataset RGB nao foi encontrada. Verifique o caminho informado em caminho_rgb."</span>)</span>
<span id="cb3-9"><a href="#cb3-9" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb3-10"><a href="#cb3-10" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb3-11"><a href="#cb3-11" aria-hidden="true" tabindex="-1"></a>caminho_rgb</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="cell-output cell-output-stdout">
<pre><code>[1] "D:/Vitor/Documents/CEAO/802/HAB/DADOS CEAO 2026/RGB"</code></pre>
</div>
</div>
</section>
<section id="leitura-dos-arquivos-de-imagem" class="level1" data-number="4">
<h1 data-number="4"><span class="header-section-number">4</span> Leitura dos arquivos de imagem</h1>
<div class="cell">
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb5"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="co"># ============================================================</span></span>
<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a><span class="co"># 3. Listar imagens</span></span>
<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a><span class="co"># ============================================================</span></span>
<span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb5-5"><a href="#cb5-5" aria-hidden="true" tabindex="-1"></a>extensoes_imagem <span class="ot">&lt;-</span> <span class="st">"</span><span class="sc">\\</span><span class="st">.(jpg|jpeg|png|bmp|tif|tiff)$"</span></span>
<span id="cb5-6"><a href="#cb5-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb5-7"><a href="#cb5-7" aria-hidden="true" tabindex="-1"></a>arquivos <span class="ot">&lt;-</span> <span class="fu">list.files</span>(</span>
<span id="cb5-8"><a href="#cb5-8" aria-hidden="true" tabindex="-1"></a> <span class="at">path =</span> caminho_rgb,</span>
<span id="cb5-9"><a href="#cb5-9" aria-hidden="true" tabindex="-1"></a> <span class="at">pattern =</span> extensoes_imagem,</span>
<span id="cb5-10"><a href="#cb5-10" aria-hidden="true" tabindex="-1"></a> <span class="at">recursive =</span> <span class="cn">TRUE</span>,</span>
<span id="cb5-11"><a href="#cb5-11" aria-hidden="true" tabindex="-1"></a> <span class="at">full.names =</span> <span class="cn">TRUE</span>,</span>
<span id="cb5-12"><a href="#cb5-12" aria-hidden="true" tabindex="-1"></a> <span class="at">ignore.case =</span> <span class="cn">TRUE</span></span>
<span id="cb5-13"><a href="#cb5-13" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb5-14"><a href="#cb5-14" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb5-15"><a href="#cb5-15" aria-hidden="true" tabindex="-1"></a><span class="cf">if</span> (<span class="fu">length</span>(arquivos) <span class="sc">==</span> <span class="dv">0</span>) {</span>
<span id="cb5-16"><a href="#cb5-16" aria-hidden="true" tabindex="-1"></a> <span class="fu">stop</span>(<span class="st">"Nenhuma imagem foi encontrada na pasta RGB."</span>)</span>
<span id="cb5-17"><a href="#cb5-17" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb5-18"><a href="#cb5-18" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb5-19"><a href="#cb5-19" aria-hidden="true" tabindex="-1"></a><span class="fu">length</span>(arquivos)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="cell-output cell-output-stdout">
<pre><code>[1] 1248</code></pre>
</div>
</div>
<div class="cell">
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb7"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="co"># ============================================================</span></span>
<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a><span class="co"># 4. Criar metadados</span></span>
<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a><span class="co"># ============================================================</span></span>
<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-5"><a href="#cb7-5" aria-hidden="true" tabindex="-1"></a>obter_classe <span class="ot">&lt;-</span> <span class="cf">function</span>(arquivo, raiz) {</span>
<span id="cb7-6"><a href="#cb7-6" aria-hidden="true" tabindex="-1"></a> arquivo_norm <span class="ot">&lt;-</span> <span class="fu">normalizePath</span>(arquivo, <span class="at">winslash =</span> <span class="st">"/"</span>, <span class="at">mustWork =</span> <span class="cn">FALSE</span>)</span>
<span id="cb7-7"><a href="#cb7-7" aria-hidden="true" tabindex="-1"></a> raiz_norm <span class="ot">&lt;-</span> <span class="fu">normalizePath</span>(raiz, <span class="at">winslash =</span> <span class="st">"/"</span>, <span class="at">mustWork =</span> <span class="cn">FALSE</span>)</span>
<span id="cb7-8"><a href="#cb7-8" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-9"><a href="#cb7-9" aria-hidden="true" tabindex="-1"></a> nome_sem_extensao <span class="ot">&lt;-</span> tools<span class="sc">::</span><span class="fu">file_path_sans_ext</span>(<span class="fu">basename</span>(arquivo))</span>
<span id="cb7-10"><a href="#cb7-10" aria-hidden="true" tabindex="-1"></a> classe_inferida <span class="ot">&lt;-</span> stringr<span class="sc">::</span><span class="fu">str_extract</span>(nome_sem_extensao, <span class="st">"^C</span><span class="sc">\\</span><span class="st">d+"</span>)</span>
<span id="cb7-11"><a href="#cb7-11" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-12"><a href="#cb7-12" aria-hidden="true" tabindex="-1"></a> <span class="cf">if</span> (<span class="sc">!</span><span class="fu">is.na</span>(classe_inferida)) {</span>
<span id="cb7-13"><a href="#cb7-13" aria-hidden="true" tabindex="-1"></a> <span class="fu">return</span>(classe_inferida)</span>
<span id="cb7-14"><a href="#cb7-14" aria-hidden="true" tabindex="-1"></a> }</span>
<span id="cb7-15"><a href="#cb7-15" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-16"><a href="#cb7-16" aria-hidden="true" tabindex="-1"></a> caminho_relativo <span class="ot">&lt;-</span> stringr<span class="sc">::</span><span class="fu">str_remove</span>(</span>
<span id="cb7-17"><a href="#cb7-17" aria-hidden="true" tabindex="-1"></a> arquivo_norm,</span>
<span id="cb7-18"><a href="#cb7-18" aria-hidden="true" tabindex="-1"></a> <span class="fu">paste0</span>(<span class="st">"^"</span>, stringr<span class="sc">::</span><span class="fu">fixed</span>(raiz_norm), <span class="st">"/?"</span>)</span>
<span id="cb7-19"><a href="#cb7-19" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb7-20"><a href="#cb7-20" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-21"><a href="#cb7-21" aria-hidden="true" tabindex="-1"></a> partes <span class="ot">&lt;-</span> <span class="fu">strsplit</span>(caminho_relativo, <span class="st">"/"</span>, <span class="at">fixed =</span> <span class="cn">TRUE</span>)[[<span class="dv">1</span>]]</span>
<span id="cb7-22"><a href="#cb7-22" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-23"><a href="#cb7-23" aria-hidden="true" tabindex="-1"></a> <span class="cf">if</span> (<span class="fu">length</span>(partes) <span class="sc">&gt;=</span> <span class="dv">2</span>) {</span>
<span id="cb7-24"><a href="#cb7-24" aria-hidden="true" tabindex="-1"></a> <span class="fu">return</span>(partes[<span class="dv">1</span>])</span>
<span id="cb7-25"><a href="#cb7-25" aria-hidden="true" tabindex="-1"></a> }</span>
<span id="cb7-26"><a href="#cb7-26" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-27"><a href="#cb7-27" aria-hidden="true" tabindex="-1"></a> stringr<span class="sc">::</span><span class="fu">str_extract</span>(nome_sem_extensao, <span class="st">"^[^_-]+"</span>)</span>
<span id="cb7-28"><a href="#cb7-28" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb7-29"><a href="#cb7-29" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-30"><a href="#cb7-30" aria-hidden="true" tabindex="-1"></a>metadados_completo <span class="ot">&lt;-</span> <span class="fu">tibble</span>(</span>
<span id="cb7-31"><a href="#cb7-31" aria-hidden="true" tabindex="-1"></a> <span class="at">arquivo =</span> arquivos,</span>
<span id="cb7-32"><a href="#cb7-32" aria-hidden="true" tabindex="-1"></a> <span class="at">classe =</span> <span class="fu">map_chr</span>(arquivos, obter_classe, <span class="at">raiz =</span> caminho_rgb)</span>
<span id="cb7-33"><a href="#cb7-33" aria-hidden="true" tabindex="-1"></a>) <span class="sc">%&gt;%</span></span>
<span id="cb7-34"><a href="#cb7-34" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(</span>
<span id="cb7-35"><a href="#cb7-35" aria-hidden="true" tabindex="-1"></a> <span class="at">classe =</span> <span class="fu">factor</span>(classe, <span class="at">levels =</span> <span class="fu">paste0</span>(<span class="st">"C"</span>, <span class="dv">1</span><span class="sc">:</span><span class="dv">13</span>)),</span>
<span id="cb7-36"><a href="#cb7-36" aria-hidden="true" tabindex="-1"></a> <span class="at">tamanho_mb =</span> <span class="fu">file.info</span>(arquivo)<span class="sc">$</span>size <span class="sc">/</span> <span class="dv">1024</span><span class="sc">^</span><span class="dv">2</span></span>
<span id="cb7-37"><a href="#cb7-37" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">%&gt;%</span></span>
<span id="cb7-38"><a href="#cb7-38" aria-hidden="true" tabindex="-1"></a> <span class="fu">arrange</span>(classe, arquivo)</span>
<span id="cb7-39"><a href="#cb7-39" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-40"><a href="#cb7-40" aria-hidden="true" tabindex="-1"></a>resumo_dataset <span class="ot">&lt;-</span> metadados_completo <span class="sc">%&gt;%</span></span>
<span id="cb7-41"><a href="#cb7-41" aria-hidden="true" tabindex="-1"></a> <span class="fu">group_by</span>(classe) <span class="sc">%&gt;%</span></span>
<span id="cb7-42"><a href="#cb7-42" aria-hidden="true" tabindex="-1"></a> <span class="fu">summarise</span>(</span>
<span id="cb7-43"><a href="#cb7-43" aria-hidden="true" tabindex="-1"></a> <span class="at">n_imagens =</span> <span class="fu">n</span>(),</span>
<span id="cb7-44"><a href="#cb7-44" aria-hidden="true" tabindex="-1"></a> <span class="at">tamanho_total_mb =</span> <span class="fu">sum</span>(tamanho_mb, <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb7-45"><a href="#cb7-45" aria-hidden="true" tabindex="-1"></a> <span class="at">tamanho_medio_mb =</span> <span class="fu">mean</span>(tamanho_mb, <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb7-46"><a href="#cb7-46" aria-hidden="true" tabindex="-1"></a> <span class="at">.groups =</span> <span class="st">"drop"</span></span>
<span id="cb7-47"><a href="#cb7-47" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb7-48"><a href="#cb7-48" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-49"><a href="#cb7-49" aria-hidden="true" tabindex="-1"></a>resumo_dataset <span class="sc">%&gt;%</span></span>
<span id="cb7-50"><a href="#cb7-50" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="fu">across</span>(<span class="fu">where</span>(is.numeric), <span class="sc">~</span> <span class="fu">round</span>(.x, <span class="dv">2</span>))) <span class="sc">%&gt;%</span></span>
<span id="cb7-51"><a href="#cb7-51" aria-hidden="true" tabindex="-1"></a> <span class="fu">kable</span>()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="cell-output-display">
<table class="caption-top table table-sm table-striped small">
<thead>
<tr class="header">
<th style="text-align: left;">classe</th>
<th style="text-align: right;">n_imagens</th>
<th style="text-align: right;">tamanho_total_mb</th>
<th style="text-align: right;">tamanho_medio_mb</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">C1</td>
<td style="text-align: right;">96</td>
<td style="text-align: right;">3388.31</td>
<td style="text-align: right;">35.29</td>
</tr>
<tr class="even">
<td style="text-align: left;">C2</td>
<td style="text-align: right;">96</td>
<td style="text-align: right;">3388.31</td>
<td style="text-align: right;">35.29</td>
</tr>
<tr class="odd">
<td style="text-align: left;">C3</td>
<td style="text-align: right;">96</td>
<td style="text-align: right;">3388.31</td>
<td style="text-align: right;">35.29</td>
</tr>
<tr class="even">
<td style="text-align: left;">C4</td>
<td style="text-align: right;">96</td>
<td style="text-align: right;">3388.31</td>
<td style="text-align: right;">35.29</td>
</tr>
<tr class="odd">
<td style="text-align: left;">C5</td>
<td style="text-align: right;">96</td>
<td style="text-align: right;">3388.31</td>
<td style="text-align: right;">35.29</td>
</tr>
<tr class="even">
<td style="text-align: left;">C6</td>
<td style="text-align: right;">96</td>
<td style="text-align: right;">3388.31</td>
<td style="text-align: right;">35.29</td>
</tr>
<tr class="odd">
<td style="text-align: left;">C7</td>
<td style="text-align: right;">96</td>
<td style="text-align: right;">3388.31</td>
<td style="text-align: right;">35.29</td>
</tr>
<tr class="even">
<td style="text-align: left;">C8</td>
<td style="text-align: right;">96</td>
<td style="text-align: right;">3388.31</td>
<td style="text-align: right;">35.29</td>
</tr>
<tr class="odd">
<td style="text-align: left;">C9</td>
<td style="text-align: right;">96</td>
<td style="text-align: right;">3388.31</td>
<td style="text-align: right;">35.29</td>
</tr>
<tr class="even">
<td style="text-align: left;">C10</td>
<td style="text-align: right;">96</td>
<td style="text-align: right;">3388.31</td>
<td style="text-align: right;">35.29</td>
</tr>
<tr class="odd">
<td style="text-align: left;">C11</td>
<td style="text-align: right;">96</td>
<td style="text-align: right;">3388.31</td>
<td style="text-align: right;">35.29</td>
</tr>
<tr class="even">
<td style="text-align: left;">C12</td>
<td style="text-align: right;">96</td>
<td style="text-align: right;">3388.31</td>
<td style="text-align: right;">35.29</td>
</tr>
<tr class="odd">
<td style="text-align: left;">C13</td>
<td style="text-align: right;">96</td>
<td style="text-align: right;">3388.31</td>
<td style="text-align: right;">35.29</td>
</tr>
</tbody>
</table>
</div>
</div>
</section>
<section id="amostra-completa-sem-limite-por-classe" class="level1" data-number="5">
<h1 data-number="5"><span class="header-section-number">5</span> Amostra completa (sem limite por classe)</h1>
<div class="cell">
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb8"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="co"># ============================================================</span></span>
<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a><span class="co"># 5. Usar TODAS as imagens disponíveis por classe</span></span>
<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a><span class="co"># ============================================================</span></span>
<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">123</span>)</span>
<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a><span class="co"># Alteracao em relacao ao original: max_imagens_por_classe = 9999</span></span>
<span id="cb8-8"><a href="#cb8-8" aria-hidden="true" tabindex="-1"></a><span class="co"># para usar todas as imagens disponiveis</span></span>
<span id="cb8-9"><a href="#cb8-9" aria-hidden="true" tabindex="-1"></a>max_imagens_por_classe <span class="ot">&lt;-</span> <span class="dv">9999</span></span>
<span id="cb8-10"><a href="#cb8-10" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb8-11"><a href="#cb8-11" aria-hidden="true" tabindex="-1"></a>metadados <span class="ot">&lt;-</span> metadados_completo <span class="sc">%&gt;%</span></span>
<span id="cb8-12"><a href="#cb8-12" aria-hidden="true" tabindex="-1"></a> <span class="fu">group_by</span>(classe) <span class="sc">%&gt;%</span></span>
<span id="cb8-13"><a href="#cb8-13" aria-hidden="true" tabindex="-1"></a> <span class="fu">group_modify</span>(<span class="sc">~</span> {</span>
<span id="cb8-14"><a href="#cb8-14" aria-hidden="true" tabindex="-1"></a> qtd <span class="ot">&lt;-</span> <span class="fu">min</span>(max_imagens_por_classe, <span class="fu">nrow</span>(.x))</span>
<span id="cb8-15"><a href="#cb8-15" aria-hidden="true" tabindex="-1"></a> <span class="fu">slice_sample</span>(.x, <span class="at">n =</span> qtd)</span>
<span id="cb8-16"><a href="#cb8-16" aria-hidden="true" tabindex="-1"></a> }) <span class="sc">%&gt;%</span></span>
<span id="cb8-17"><a href="#cb8-17" aria-hidden="true" tabindex="-1"></a> <span class="fu">ungroup</span>() <span class="sc">%&gt;%</span></span>
<span id="cb8-18"><a href="#cb8-18" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">classe =</span> <span class="fu">factor</span>(classe, <span class="at">levels =</span> <span class="fu">paste0</span>(<span class="st">"C"</span>, <span class="dv">1</span><span class="sc">:</span><span class="dv">13</span>)))</span>
<span id="cb8-19"><a href="#cb8-19" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb8-20"><a href="#cb8-20" aria-hidden="true" tabindex="-1"></a><span class="fu">cat</span>(<span class="st">"Total de imagens no dataset completo:"</span>, <span class="fu">nrow</span>(metadados_completo), <span class="st">"</span><span class="sc">\n</span><span class="st">"</span>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="cell-output cell-output-stdout">
<pre><code>Total de imagens no dataset completo: 1248 </code></pre>
</div>
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb10"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a><span class="fu">cat</span>(<span class="st">"Total de imagens na amostra:"</span>, <span class="fu">nrow</span>(metadados), <span class="st">"</span><span class="sc">\n</span><span class="st">"</span>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="cell-output cell-output-stdout">
<pre><code>Total de imagens na amostra: 1248 </code></pre>
</div>
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb12"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a>metadados <span class="sc">%&gt;%</span></span>
<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">count</span>(classe, <span class="at">name =</span> <span class="st">"n_imagens_amostra"</span>) <span class="sc">%&gt;%</span></span>
<span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">arrange</span>(classe) <span class="sc">%&gt;%</span></span>
<span id="cb12-4"><a href="#cb12-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">kable</span>()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="cell-output-display">
<table class="caption-top table table-sm table-striped small">
<thead>
<tr class="header">
<th style="text-align: left;">classe</th>
<th style="text-align: right;">n_imagens_amostra</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">C1</td>
<td style="text-align: right;">96</td>
</tr>
<tr class="even">
<td style="text-align: left;">C2</td>
<td style="text-align: right;">96</td>
</tr>
<tr class="odd">
<td style="text-align: left;">C3</td>
<td style="text-align: right;">96</td>
</tr>
<tr class="even">
<td style="text-align: left;">C4</td>
<td style="text-align: right;">96</td>
</tr>
<tr class="odd">
<td style="text-align: left;">C5</td>
<td style="text-align: right;">96</td>
</tr>
<tr class="even">
<td style="text-align: left;">C6</td>
<td style="text-align: right;">96</td>
</tr>
<tr class="odd">
<td style="text-align: left;">C7</td>
<td style="text-align: right;">96</td>
</tr>
<tr class="even">
<td style="text-align: left;">C8</td>
<td style="text-align: right;">96</td>
</tr>
<tr class="odd">
<td style="text-align: left;">C9</td>
<td style="text-align: right;">96</td>
</tr>
<tr class="even">
<td style="text-align: left;">C10</td>
<td style="text-align: right;">96</td>
</tr>
<tr class="odd">
<td style="text-align: left;">C11</td>
<td style="text-align: right;">96</td>
</tr>
<tr class="even">
<td style="text-align: left;">C12</td>
<td style="text-align: right;">96</td>
</tr>
<tr class="odd">
<td style="text-align: left;">C13</td>
<td style="text-align: right;">96</td>
</tr>
</tbody>
</table>
</div>
</div>
<div class="cell">
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb13"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(metadados, <span class="fu">aes</span>(<span class="at">x =</span> classe)) <span class="sc">+</span></span>
<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_bar</span>(<span class="at">fill =</span> <span class="st">"#2C7FB8"</span>) <span class="sc">+</span></span>
<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">coord_flip</span>() <span class="sc">+</span></span>
<span id="cb13-4"><a href="#cb13-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
<span id="cb13-5"><a href="#cb13-5" aria-hidden="true" tabindex="-1"></a> <span class="at">title =</span> <span class="st">"Distribuicao de imagens por classe (dataset completo)"</span>,</span>
<span id="cb13-6"><a href="#cb13-6" aria-hidden="true" tabindex="-1"></a> <span class="at">x =</span> <span class="st">"Classe"</span>,</span>
<span id="cb13-7"><a href="#cb13-7" aria-hidden="true" tabindex="-1"></a> <span class="at">y =</span> <span class="st">"Numero de imagens"</span></span>
<span id="cb13-8"><a href="#cb13-8" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">+</span></span>
<span id="cb13-9"><a href="#cb13-9" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme_minimal</span>()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="trabalho_svm_rgb_completo_files/figure-html/unnamed-chunk-6-1.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
</div>
</section>
<section id="modelo-pre-treinado-transfer-learning" class="level1" data-number="6">
<h1 data-number="6"><span class="header-section-number">6</span> Modelo pre-treinado (Transfer Learning)</h1>
<div class="cell">
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb14"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a><span class="co"># ============================================================</span></span>
<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a><span class="co"># 6. Carregar modelo pre-treinado MobileNetV2</span></span>
<span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a><span class="co"># ============================================================</span></span>
<span id="cb14-4"><a href="#cb14-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb14-5"><a href="#cb14-5" aria-hidden="true" tabindex="-1"></a><span class="fu">dir.create</span>(<span class="st">"outputs"</span>, <span class="at">showWarnings =</span> <span class="cn">FALSE</span>)</span>
<span id="cb14-6"><a href="#cb14-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb14-7"><a href="#cb14-7" aria-hidden="true" tabindex="-1"></a>modelo_base <span class="ot">&lt;-</span> <span class="fu">application_mobilenet_v2</span>(</span>
<span id="cb14-8"><a href="#cb14-8" aria-hidden="true" tabindex="-1"></a> <span class="at">include_top =</span> <span class="cn">FALSE</span>,</span>
<span id="cb14-9"><a href="#cb14-9" aria-hidden="true" tabindex="-1"></a> <span class="at">weights =</span> <span class="st">"imagenet"</span>,</span>
<span id="cb14-10"><a href="#cb14-10" aria-hidden="true" tabindex="-1"></a> <span class="at">pooling =</span> <span class="st">"avg"</span>,</span>
<span id="cb14-11"><a href="#cb14-11" aria-hidden="true" tabindex="-1"></a> <span class="at">input_shape =</span> <span class="fu">c</span>(<span class="dv">224</span>L, <span class="dv">224</span>L, <span class="dv">3</span>L)</span>
<span id="cb14-12"><a href="#cb14-12" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb14-13"><a href="#cb14-13" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb14-14"><a href="#cb14-14" aria-hidden="true" tabindex="-1"></a><span class="fu">cat</span>(<span class="st">"Modelo carregado:"</span>, modelo_base<span class="sc">$</span>name, <span class="st">"</span><span class="sc">\n</span><span class="st">"</span>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="cell-output cell-output-stdout">
<pre><code>Modelo carregado: mobilenetv2_1.00_224 </code></pre>
</div>
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb16"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a><span class="fu">cat</span>(<span class="st">"Dimensao do vetor de features:"</span>, modelo_base<span class="sc">$</span>output_shape[[<span class="dv">2</span>]], <span class="st">"</span><span class="sc">\n</span><span class="st">"</span>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="cell-output cell-output-stdout">
<pre><code>Dimensao do vetor de features: 1280 </code></pre>
</div>
</div>
</section>
<section id="extracao-de-atributos-por-transfer-learning" class="level1" data-number="7">
<h1 data-number="7"><span class="header-section-number">7</span> Extracao de atributos por Transfer Learning</h1>
<div class="cell">
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb18"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a><span class="co"># ============================================================</span></span>
<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a><span class="co"># 7. Extracao de features (com cache separado do original)</span></span>
<span id="cb18-3"><a href="#cb18-3" aria-hidden="true" tabindex="-1"></a><span class="co"># ============================================================</span></span>
<span id="cb18-4"><a href="#cb18-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb18-5"><a href="#cb18-5" aria-hidden="true" tabindex="-1"></a>pasta_saida <span class="ot">&lt;-</span> <span class="st">"outputs/completo"</span></span>
<span id="cb18-6"><a href="#cb18-6" aria-hidden="true" tabindex="-1"></a><span class="fu">dir.create</span>(pasta_saida, <span class="at">recursive =</span> <span class="cn">TRUE</span>, <span class="at">showWarnings =</span> <span class="cn">FALSE</span>)</span>
<span id="cb18-7"><a href="#cb18-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb18-8"><a href="#cb18-8" aria-hidden="true" tabindex="-1"></a><span class="co"># Nomes diferentes para nao sobrescrever o cache do trabalho original</span></span>
<span id="cb18-9"><a href="#cb18-9" aria-hidden="true" tabindex="-1"></a>arquivo_cache_features <span class="ot">&lt;-</span> <span class="fu">file.path</span>(pasta_saida, <span class="st">"features_rgb_mobilenetv2_completo.rds"</span>)</span>
<span id="cb18-10"><a href="#cb18-10" aria-hidden="true" tabindex="-1"></a>arquivo_cache_parcial <span class="ot">&lt;-</span> <span class="fu">file.path</span>(pasta_saida, <span class="st">"features_rgb_mobilenetv2_completo_temp.rds"</span>)</span>
<span id="cb18-11"><a href="#cb18-11" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb18-12"><a href="#cb18-12" aria-hidden="true" tabindex="-1"></a>tamanho_imagem <span class="ot">&lt;-</span> <span class="dv">224</span>L</span>
<span id="cb18-13"><a href="#cb18-13" aria-hidden="true" tabindex="-1"></a>tamanho_lote <span class="ot">&lt;-</span> <span class="dv">16</span>L</span>
<span id="cb18-14"><a href="#cb18-14" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb18-15"><a href="#cb18-15" aria-hidden="true" tabindex="-1"></a>extrair_features_batch <span class="ot">&lt;-</span> <span class="cf">function</span>(arquivos, modelo, <span class="at">tamanho =</span> <span class="dv">224</span>L, <span class="at">lote =</span> <span class="dv">16</span>L) {</span>
<span id="cb18-16"><a href="#cb18-16" aria-hidden="true" tabindex="-1"></a> n <span class="ot">&lt;-</span> <span class="fu">length</span>(arquivos)</span>
<span id="cb18-17"><a href="#cb18-17" aria-hidden="true" tabindex="-1"></a> resultados <span class="ot">&lt;-</span> <span class="fu">matrix</span>(<span class="cn">NA_real_</span>, <span class="at">nrow =</span> n, <span class="at">ncol =</span> <span class="fu">as.integer</span>(modelo<span class="sc">$</span>output_shape[[<span class="dv">2</span>]]))</span>
<span id="cb18-18"><a href="#cb18-18" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb18-19"><a href="#cb18-19" aria-hidden="true" tabindex="-1"></a> <span class="cf">for</span> (inicio <span class="cf">in</span> <span class="fu">seq</span>(<span class="dv">1</span>, n, <span class="at">by =</span> lote)) {</span>
<span id="cb18-20"><a href="#cb18-20" aria-hidden="true" tabindex="-1"></a> fim <span class="ot">&lt;-</span> <span class="fu">min</span>(inicio <span class="sc">+</span> lote <span class="sc">-</span> <span class="dv">1</span>L, n)</span>
<span id="cb18-21"><a href="#cb18-21" aria-hidden="true" tabindex="-1"></a> bloco <span class="ot">&lt;-</span> arquivos[inicio<span class="sc">:</span>fim]</span>
<span id="cb18-22"><a href="#cb18-22" aria-hidden="true" tabindex="-1"></a> tam_bloco <span class="ot">&lt;-</span> <span class="fu">length</span>(bloco)</span>
<span id="cb18-23"><a href="#cb18-23" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb18-24"><a href="#cb18-24" aria-hidden="true" tabindex="-1"></a> batch_array <span class="ot">&lt;-</span> <span class="fu">array</span>(<span class="dv">0</span>, <span class="at">dim =</span> <span class="fu">c</span>(tam_bloco, tamanho, tamanho, <span class="dv">3</span>L))</span>
<span id="cb18-25"><a href="#cb18-25" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb18-26"><a href="#cb18-26" aria-hidden="true" tabindex="-1"></a> <span class="cf">for</span> (j <span class="cf">in</span> <span class="fu">seq_len</span>(tam_bloco)) {</span>
<span id="cb18-27"><a href="#cb18-27" aria-hidden="true" tabindex="-1"></a> img <span class="ot">&lt;-</span> <span class="fu">image_load</span>(bloco[j], <span class="at">target_size =</span> <span class="fu">c</span>(tamanho, tamanho))</span>
<span id="cb18-28"><a href="#cb18-28" aria-hidden="true" tabindex="-1"></a> batch_array[j, , , ] <span class="ot">&lt;-</span> <span class="fu">image_to_array</span>(img)</span>
<span id="cb18-29"><a href="#cb18-29" aria-hidden="true" tabindex="-1"></a> }</span>
<span id="cb18-30"><a href="#cb18-30" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb18-31"><a href="#cb18-31" aria-hidden="true" tabindex="-1"></a> batch_prep <span class="ot">&lt;-</span> (batch_array <span class="sc">/</span> <span class="fl">127.5</span>) <span class="sc">-</span> <span class="fl">1.0</span></span>
<span id="cb18-32"><a href="#cb18-32" aria-hidden="true" tabindex="-1"></a> features <span class="ot">&lt;-</span> <span class="fu">predict</span>(modelo, batch_prep, <span class="at">verbose =</span> <span class="dv">0</span>L)</span>
<span id="cb18-33"><a href="#cb18-33" aria-hidden="true" tabindex="-1"></a> resultados[inicio<span class="sc">:</span>fim, ] <span class="ot">&lt;-</span> features</span>
<span id="cb18-34"><a href="#cb18-34" aria-hidden="true" tabindex="-1"></a> }</span>
<span id="cb18-35"><a href="#cb18-35" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb18-36"><a href="#cb18-36" aria-hidden="true" tabindex="-1"></a> resultados</span>
<span id="cb18-37"><a href="#cb18-37" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb18-38"><a href="#cb18-38" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb18-39"><a href="#cb18-39" aria-hidden="true" tabindex="-1"></a><span class="cf">if</span> (<span class="fu">file.exists</span>(arquivo_cache_features)) {</span>
<span id="cb18-40"><a href="#cb18-40" aria-hidden="true" tabindex="-1"></a> dados <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(arquivo_cache_features)</span>
<span id="cb18-41"><a href="#cb18-41" aria-hidden="true" tabindex="-1"></a> <span class="fu">cat</span>(<span class="st">"Features carregadas do cache:"</span>, arquivo_cache_features, <span class="st">"</span><span class="sc">\n</span><span class="st">"</span>)</span>
<span id="cb18-42"><a href="#cb18-42" aria-hidden="true" tabindex="-1"></a>} <span class="cf">else</span> {</span>
<span id="cb18-43"><a href="#cb18-43" aria-hidden="true" tabindex="-1"></a> <span class="fu">cat</span>(<span class="st">"Extraindo features com MobileNetV2. Isso pode levar alguns minutos...</span><span class="sc">\n</span><span class="st">"</span>)</span>
<span id="cb18-44"><a href="#cb18-44" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb18-45"><a href="#cb18-45" aria-hidden="true" tabindex="-1"></a> erros_idx <span class="ot">&lt;-</span> <span class="fu">integer</span>(<span class="dv">0</span>)</span>
<span id="cb18-46"><a href="#cb18-46" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb18-47"><a href="#cb18-47" aria-hidden="true" tabindex="-1"></a> <span class="cf">if</span> (<span class="fu">file.exists</span>(arquivo_cache_parcial)) {</span>
<span id="cb18-48"><a href="#cb18-48" aria-hidden="true" tabindex="-1"></a> features_matrix <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(arquivo_cache_parcial)</span>
<span id="cb18-49"><a href="#cb18-49" aria-hidden="true" tabindex="-1"></a> linhas_prontas <span class="ot">&lt;-</span> <span class="fu">which</span>(<span class="fu">complete.cases</span>(features_matrix))</span>
<span id="cb18-50"><a href="#cb18-50" aria-hidden="true" tabindex="-1"></a> <span class="fu">cat</span>(<span class="st">"Cache parcial encontrado:"</span>, <span class="fu">length</span>(linhas_prontas), <span class="st">"imagens ja processadas.</span><span class="sc">\n</span><span class="st">"</span>)</span>
<span id="cb18-51"><a href="#cb18-51" aria-hidden="true" tabindex="-1"></a> } <span class="cf">else</span> {</span>
<span id="cb18-52"><a href="#cb18-52" aria-hidden="true" tabindex="-1"></a> n_features <span class="ot">&lt;-</span> <span class="fu">as.integer</span>(modelo_base<span class="sc">$</span>output_shape[[<span class="dv">2</span>]])</span>
<span id="cb18-53"><a href="#cb18-53" aria-hidden="true" tabindex="-1"></a> features_matrix <span class="ot">&lt;-</span> <span class="fu">matrix</span>(<span class="cn">NA_real_</span>, <span class="at">nrow =</span> <span class="fu">nrow</span>(metadados), <span class="at">ncol =</span> n_features)</span>
<span id="cb18-54"><a href="#cb18-54" aria-hidden="true" tabindex="-1"></a> linhas_prontas <span class="ot">&lt;-</span> <span class="fu">integer</span>(<span class="dv">0</span>)</span>
<span id="cb18-55"><a href="#cb18-55" aria-hidden="true" tabindex="-1"></a> }</span>
<span id="cb18-56"><a href="#cb18-56" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb18-57"><a href="#cb18-57" aria-hidden="true" tabindex="-1"></a> linhas_pendentes <span class="ot">&lt;-</span> <span class="fu">setdiff</span>(<span class="fu">seq_len</span>(<span class="fu">nrow</span>(metadados)), linhas_prontas)</span>
<span id="cb18-58"><a href="#cb18-58" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb18-59"><a href="#cb18-59" aria-hidden="true" tabindex="-1"></a> <span class="cf">if</span> (<span class="fu">length</span>(linhas_pendentes) <span class="sc">&gt;</span> <span class="dv">0</span>) {</span>
<span id="cb18-60"><a href="#cb18-60" aria-hidden="true" tabindex="-1"></a> <span class="cf">for</span> (inicio <span class="cf">in</span> <span class="fu">seq</span>(<span class="dv">1</span>, <span class="fu">length</span>(linhas_pendentes), <span class="at">by =</span> tamanho_lote)) {</span>
<span id="cb18-61"><a href="#cb18-61" aria-hidden="true" tabindex="-1"></a> fim <span class="ot">&lt;-</span> <span class="fu">min</span>(inicio <span class="sc">+</span> tamanho_lote <span class="sc">-</span> <span class="dv">1</span>L, <span class="fu">length</span>(linhas_pendentes))</span>
<span id="cb18-62"><a href="#cb18-62" aria-hidden="true" tabindex="-1"></a> idxs <span class="ot">&lt;-</span> linhas_pendentes[inicio<span class="sc">:</span>fim]</span>
<span id="cb18-63"><a href="#cb18-63" aria-hidden="true" tabindex="-1"></a> arquivos_bloco <span class="ot">&lt;-</span> metadados<span class="sc">$</span>arquivo[idxs]</span>
<span id="cb18-64"><a href="#cb18-64" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb18-65"><a href="#cb18-65" aria-hidden="true" tabindex="-1"></a> resultado <span class="ot">&lt;-</span> <span class="fu">tryCatch</span>(</span>
<span id="cb18-66"><a href="#cb18-66" aria-hidden="true" tabindex="-1"></a> <span class="fu">extrair_features_batch</span>(arquivos_bloco, modelo_base, tamanho_imagem, <span class="fu">length</span>(idxs)),</span>
<span id="cb18-67"><a href="#cb18-67" aria-hidden="true" tabindex="-1"></a> <span class="at">error =</span> <span class="cf">function</span>(e) {</span>
<span id="cb18-68"><a href="#cb18-68" aria-hidden="true" tabindex="-1"></a> <span class="fu">message</span>(<span class="st">"Erro no lote "</span>, inicio, <span class="st">"-"</span>, fim, <span class="st">": "</span>, <span class="fu">conditionMessage</span>(e))</span>
<span id="cb18-69"><a href="#cb18-69" aria-hidden="true" tabindex="-1"></a> erros_idx <span class="ot">&lt;&lt;-</span> <span class="fu">c</span>(erros_idx, idxs)</span>
<span id="cb18-70"><a href="#cb18-70" aria-hidden="true" tabindex="-1"></a> <span class="cn">NULL</span></span>
<span id="cb18-71"><a href="#cb18-71" aria-hidden="true" tabindex="-1"></a> }</span>
<span id="cb18-72"><a href="#cb18-72" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb18-73"><a href="#cb18-73" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb18-74"><a href="#cb18-74" aria-hidden="true" tabindex="-1"></a> <span class="cf">if</span> (<span class="sc">!</span><span class="fu">is.null</span>(resultado)) {</span>
<span id="cb18-75"><a href="#cb18-75" aria-hidden="true" tabindex="-1"></a> features_matrix[idxs, ] <span class="ot">&lt;-</span> resultado</span>
<span id="cb18-76"><a href="#cb18-76" aria-hidden="true" tabindex="-1"></a> }</span>
<span id="cb18-77"><a href="#cb18-77" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb18-78"><a href="#cb18-78" aria-hidden="true" tabindex="-1"></a> <span class="fu">cat</span>(<span class="fu">sprintf</span>(<span class="st">" Processadas: %d / %d</span><span class="sc">\r</span><span class="st">"</span>, <span class="fu">min</span>(fim, <span class="fu">length</span>(linhas_pendentes)), <span class="fu">length</span>(linhas_pendentes)))</span>
<span id="cb18-79"><a href="#cb18-79" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb18-80"><a href="#cb18-80" aria-hidden="true" tabindex="-1"></a> <span class="cf">if</span> (fim <span class="sc">%%</span> (tamanho_lote <span class="sc">*</span> <span class="dv">5</span>L) <span class="sc">==</span> <span class="dv">0</span>L) {</span>
<span id="cb18-81"><a href="#cb18-81" aria-hidden="true" tabindex="-1"></a> <span class="fu">saveRDS</span>(features_matrix, arquivo_cache_parcial)</span>
<span id="cb18-82"><a href="#cb18-82" aria-hidden="true" tabindex="-1"></a> }</span>
<span id="cb18-83"><a href="#cb18-83" aria-hidden="true" tabindex="-1"></a> }</span>
<span id="cb18-84"><a href="#cb18-84" aria-hidden="true" tabindex="-1"></a> <span class="fu">cat</span>(<span class="st">"</span><span class="sc">\n</span><span class="st">"</span>)</span>
<span id="cb18-85"><a href="#cb18-85" aria-hidden="true" tabindex="-1"></a> <span class="fu">saveRDS</span>(features_matrix, arquivo_cache_parcial)</span>
<span id="cb18-86"><a href="#cb18-86" aria-hidden="true" tabindex="-1"></a> }</span>
<span id="cb18-87"><a href="#cb18-87" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb18-88"><a href="#cb18-88" aria-hidden="true" tabindex="-1"></a> imagens_ok <span class="ot">&lt;-</span> <span class="fu">complete.cases</span>(features_matrix)</span>
<span id="cb18-89"><a href="#cb18-89" aria-hidden="true" tabindex="-1"></a> n_features <span class="ot">&lt;-</span> <span class="fu">ncol</span>(features_matrix)</span>
<span id="cb18-90"><a href="#cb18-90" aria-hidden="true" tabindex="-1"></a> nomes_feat <span class="ot">&lt;-</span> <span class="fu">paste0</span>(<span class="st">"feat_"</span>, stringr<span class="sc">::</span><span class="fu">str_pad</span>(<span class="fu">seq_len</span>(n_features), <span class="dv">4</span>, <span class="at">pad =</span> <span class="st">"0"</span>))</span>
<span id="cb18-91"><a href="#cb18-91" aria-hidden="true" tabindex="-1"></a> <span class="fu">colnames</span>(features_matrix) <span class="ot">&lt;-</span> nomes_feat</span>
<span id="cb18-92"><a href="#cb18-92" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb18-93"><a href="#cb18-93" aria-hidden="true" tabindex="-1"></a> dados <span class="ot">&lt;-</span> <span class="fu">bind_cols</span>(</span>
<span id="cb18-94"><a href="#cb18-94" aria-hidden="true" tabindex="-1"></a> metadados[imagens_ok, ],</span>
<span id="cb18-95"><a href="#cb18-95" aria-hidden="true" tabindex="-1"></a> <span class="fu">as_tibble</span>(features_matrix[imagens_ok, ])</span>
<span id="cb18-96"><a href="#cb18-96" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb18-97"><a href="#cb18-97" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb18-98"><a href="#cb18-98" aria-hidden="true" tabindex="-1"></a> <span class="cf">if</span> (<span class="fu">length</span>(erros_idx) <span class="sc">&gt;</span> <span class="dv">0</span>) {</span>
<span id="cb18-99"><a href="#cb18-99" aria-hidden="true" tabindex="-1"></a> erros_df <span class="ot">&lt;-</span> metadados[erros_idx, ] <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">erro =</span> <span class="st">"falha na extracao CNN"</span>)</span>
<span id="cb18-100"><a href="#cb18-100" aria-hidden="true" tabindex="-1"></a> arquivo_erros <span class="ot">&lt;-</span> <span class="fu">file.path</span>(pasta_saida, <span class="st">"erros_processamento_rgb_completo.csv"</span>)</span>
<span id="cb18-101"><a href="#cb18-101" aria-hidden="true" tabindex="-1"></a> <span class="fu">write_csv</span>(erros_df, arquivo_erros)</span>
<span id="cb18-102"><a href="#cb18-102" aria-hidden="true" tabindex="-1"></a> <span class="fu">cat</span>(<span class="st">"Imagens com erro:"</span>, <span class="fu">length</span>(erros_idx), <span class="st">"(ver"</span>, arquivo_erros, <span class="st">")</span><span class="sc">\n</span><span class="st">"</span>)</span>
<span id="cb18-103"><a href="#cb18-103" aria-hidden="true" tabindex="-1"></a> }</span>
<span id="cb18-104"><a href="#cb18-104" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb18-105"><a href="#cb18-105" aria-hidden="true" tabindex="-1"></a> <span class="fu">saveRDS</span>(dados, arquivo_cache_features)</span>
<span id="cb18-106"><a href="#cb18-106" aria-hidden="true" tabindex="-1"></a> <span class="cf">if</span> (<span class="fu">file.exists</span>(arquivo_cache_parcial)) <span class="fu">file.remove</span>(arquivo_cache_parcial)</span>
<span id="cb18-107"><a href="#cb18-107" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb18-108"><a href="#cb18-108" aria-hidden="true" tabindex="-1"></a> <span class="fu">cat</span>(<span class="st">"Features salvas em:"</span>, arquivo_cache_features, <span class="st">"</span><span class="sc">\n</span><span class="st">"</span>)</span>
<span id="cb18-109"><a href="#cb18-109" aria-hidden="true" tabindex="-1"></a>}</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="cell-output cell-output-stdout">
<pre><code>Features carregadas do cache: outputs/completo/features_rgb_mobilenetv2_completo.rds </code></pre>
</div>
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb20"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a><span class="fu">cat</span>(<span class="st">"Imagens na base final:"</span>, <span class="fu">nrow</span>(dados), <span class="st">"</span><span class="sc">\n</span><span class="st">"</span>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="cell-output cell-output-stdout">
<pre><code>Imagens na base final: 1248 </code></pre>
</div>
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb22"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a><span class="fu">cat</span>(<span class="st">"Numero de atributos extraidos (MobileNetV2):"</span>, <span class="fu">ncol</span>(dados) <span class="sc">-</span> <span class="dv">3</span>L, <span class="st">"</span><span class="sc">\n</span><span class="st">"</span>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="cell-output cell-output-stdout">
<pre><code>Numero de atributos extraidos (MobileNetV2): 1280 </code></pre>
</div>
</div>
</section>
<section id="separacao-entre-treino-e-teste" class="level1" data-number="8">
<h1 data-number="8"><span class="header-section-number">8</span> Separacao entre treino e teste</h1>
<div class="cell">
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb24"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a><span class="co"># ============================================================</span></span>
<span id="cb24-2"><a href="#cb24-2" aria-hidden="true" tabindex="-1"></a><span class="co"># 8. Separacao treino/teste</span></span>
<span id="cb24-3"><a href="#cb24-3" aria-hidden="true" tabindex="-1"></a><span class="co"># ============================================================</span></span>
<span id="cb24-4"><a href="#cb24-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb24-5"><a href="#cb24-5" aria-hidden="true" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">123</span>)</span>
<span id="cb24-6"><a href="#cb24-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb24-7"><a href="#cb24-7" aria-hidden="true" tabindex="-1"></a>idx_treino <span class="ot">&lt;-</span> <span class="fu">createDataPartition</span>(</span>
<span id="cb24-8"><a href="#cb24-8" aria-hidden="true" tabindex="-1"></a> <span class="at">y =</span> dados<span class="sc">$</span>classe,</span>
<span id="cb24-9"><a href="#cb24-9" aria-hidden="true" tabindex="-1"></a> <span class="at">p =</span> <span class="fl">0.70</span>,</span>
<span id="cb24-10"><a href="#cb24-10" aria-hidden="true" tabindex="-1"></a> <span class="at">list =</span> <span class="cn">FALSE</span></span>
<span id="cb24-11"><a href="#cb24-11" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb24-12"><a href="#cb24-12" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb24-13"><a href="#cb24-13" aria-hidden="true" tabindex="-1"></a>treino <span class="ot">&lt;-</span> dados[idx_treino, ]</span>
<span id="cb24-14"><a href="#cb24-14" aria-hidden="true" tabindex="-1"></a>teste <span class="ot">&lt;-</span> dados[<span class="sc">-</span>idx_treino, ]</span>
<span id="cb24-15"><a href="#cb24-15" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb24-16"><a href="#cb24-16" aria-hidden="true" tabindex="-1"></a><span class="fu">cat</span>(<span class="st">"Imagens no treino:"</span>, <span class="fu">nrow</span>(treino), <span class="st">"</span><span class="sc">\n</span><span class="st">"</span>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="cell-output cell-output-stdout">
<pre><code>Imagens no treino: 884 </code></pre>
</div>
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb26"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a><span class="fu">cat</span>(<span class="st">"Imagens no teste:"</span>, <span class="fu">nrow</span>(teste), <span class="st">"</span><span class="sc">\n</span><span class="st">"</span>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="cell-output cell-output-stdout">
<pre><code>Imagens no teste: 364 </code></pre>
</div>
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb28"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1" aria-hidden="true" tabindex="-1"></a>treino <span class="sc">%&gt;%</span></span>
<span id="cb28-2"><a href="#cb28-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">count</span>(classe, <span class="at">name =</span> <span class="st">"treino"</span>) <span class="sc">%&gt;%</span></span>
<span id="cb28-3"><a href="#cb28-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">full_join</span>(</span>
<span id="cb28-4"><a href="#cb28-4" aria-hidden="true" tabindex="-1"></a> teste <span class="sc">%&gt;%</span> <span class="fu">count</span>(classe, <span class="at">name =</span> <span class="st">"teste"</span>),</span>
<span id="cb28-5"><a href="#cb28-5" aria-hidden="true" tabindex="-1"></a> <span class="at">by =</span> <span class="st">"classe"</span></span>
<span id="cb28-6"><a href="#cb28-6" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">%&gt;%</span></span>
<span id="cb28-7"><a href="#cb28-7" aria-hidden="true" tabindex="-1"></a> <span class="fu">arrange</span>(classe) <span class="sc">%&gt;%</span></span>
<span id="cb28-8"><a href="#cb28-8" aria-hidden="true" tabindex="-1"></a> <span class="fu">kable</span>()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="cell-output-display">
<table class="caption-top table table-sm table-striped small">
<thead>
<tr class="header">
<th style="text-align: left;">classe</th>
<th style="text-align: right;">treino</th>
<th style="text-align: right;">teste</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">C1</td>
<td style="text-align: right;">68</td>
<td style="text-align: right;">28</td>
</tr>
<tr class="even">
<td style="text-align: left;">C2</td>
<td style="text-align: right;">68</td>
<td style="text-align: right;">28</td>
</tr>
<tr class="odd">
<td style="text-align: left;">C3</td>
<td style="text-align: right;">68</td>
<td style="text-align: right;">28</td>
</tr>
<tr class="even">
<td style="text-align: left;">C4</td>
<td style="text-align: right;">68</td>
<td style="text-align: right;">28</td>
</tr>
<tr class="odd">
<td style="text-align: left;">C5</td>
<td style="text-align: right;">68</td>
<td style="text-align: right;">28</td>
</tr>
<tr class="even">
<td style="text-align: left;">C6</td>
<td style="text-align: right;">68</td>
<td style="text-align: right;">28</td>
</tr>
<tr class="odd">
<td style="text-align: left;">C7</td>
<td style="text-align: right;">68</td>
<td style="text-align: right;">28</td>
</tr>
<tr class="even">
<td style="text-align: left;">C8</td>
<td style="text-align: right;">68</td>
<td style="text-align: right;">28</td>
</tr>
<tr class="odd">
<td style="text-align: left;">C9</td>
<td style="text-align: right;">68</td>
<td style="text-align: right;">28</td>
</tr>
<tr class="even">
<td style="text-align: left;">C10</td>
<td style="text-align: right;">68</td>
<td style="text-align: right;">28</td>
</tr>
<tr class="odd">
<td style="text-align: left;">C11</td>
<td style="text-align: right;">68</td>
<td style="text-align: right;">28</td>
</tr>
<tr class="even">
<td style="text-align: left;">C12</td>
<td style="text-align: right;">68</td>
<td style="text-align: right;">28</td>
</tr>
<tr class="odd">
<td style="text-align: left;">C13</td>
<td style="text-align: right;">68</td>
<td style="text-align: right;">28</td>
</tr>
</tbody>
</table>
</div>
</div>
</section>
<section id="pre-processamento" class="level1" data-number="9">
<h1 data-number="9"><span class="header-section-number">9</span> Pre-processamento</h1>
<div class="cell">
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb29"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a><span class="co"># ============================================================</span></span>
<span id="cb29-2"><a href="#cb29-2" aria-hidden="true" tabindex="-1"></a><span class="co"># 9. Preparar matrizes X e vetor y</span></span>
<span id="cb29-3"><a href="#cb29-3" aria-hidden="true" tabindex="-1"></a><span class="co"># ============================================================</span></span>
<span id="cb29-4"><a href="#cb29-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb29-5"><a href="#cb29-5" aria-hidden="true" tabindex="-1"></a>colunas_nao_preditoras <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">"arquivo"</span>, <span class="st">"classe"</span>, <span class="st">"tamanho_mb"</span>)</span>
<span id="cb29-6"><a href="#cb29-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb29-7"><a href="#cb29-7" aria-hidden="true" tabindex="-1"></a>x_treino <span class="ot">&lt;-</span> treino <span class="sc">%&gt;%</span></span>
<span id="cb29-8"><a href="#cb29-8" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(<span class="sc">-</span><span class="fu">all_of</span>(colunas_nao_preditoras))</span>
<span id="cb29-9"><a href="#cb29-9" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb29-10"><a href="#cb29-10" aria-hidden="true" tabindex="-1"></a>x_teste <span class="ot">&lt;-</span> teste <span class="sc">%&gt;%</span></span>
<span id="cb29-11"><a href="#cb29-11" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(<span class="sc">-</span><span class="fu">all_of</span>(colunas_nao_preditoras))</span>
<span id="cb29-12"><a href="#cb29-12" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb29-13"><a href="#cb29-13" aria-hidden="true" tabindex="-1"></a>y_treino <span class="ot">&lt;-</span> <span class="fu">droplevels</span>(treino<span class="sc">$</span>classe)</span>
<span id="cb29-14"><a href="#cb29-14" aria-hidden="true" tabindex="-1"></a>y_teste <span class="ot">&lt;-</span> <span class="fu">factor</span>(teste<span class="sc">$</span>classe, <span class="at">levels =</span> <span class="fu">levels</span>(y_treino))</span>
<span id="cb29-15"><a href="#cb29-15" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb29-16"><a href="#cb29-16" aria-hidden="true" tabindex="-1"></a>variaveis_nzv <span class="ot">&lt;-</span> <span class="fu">nearZeroVar</span>(x_treino)</span>
<span id="cb29-17"><a href="#cb29-17" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb29-18"><a href="#cb29-18" aria-hidden="true" tabindex="-1"></a><span class="cf">if</span> (<span class="fu">length</span>(variaveis_nzv) <span class="sc">&gt;</span> <span class="dv">0</span>) {</span>
<span id="cb29-19"><a href="#cb29-19" aria-hidden="true" tabindex="-1"></a> x_treino <span class="ot">&lt;-</span> x_treino[, <span class="sc">-</span>variaveis_nzv, drop <span class="ot">=</span> <span class="cn">FALSE</span>]</span>
<span id="cb29-20"><a href="#cb29-20" aria-hidden="true" tabindex="-1"></a> x_teste <span class="ot">&lt;-</span> x_teste[, <span class="fu">colnames</span>(x_treino), drop <span class="ot">=</span> <span class="cn">FALSE</span>]</span>
<span id="cb29-21"><a href="#cb29-21" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb29-22"><a href="#cb29-22" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb29-23"><a href="#cb29-23" aria-hidden="true" tabindex="-1"></a>preproc <span class="ot">&lt;-</span> <span class="fu">preProcess</span>(</span>
<span id="cb29-24"><a href="#cb29-24" aria-hidden="true" tabindex="-1"></a> x_treino,</span>
<span id="cb29-25"><a href="#cb29-25" aria-hidden="true" tabindex="-1"></a> <span class="at">method =</span> <span class="fu">c</span>(<span class="st">"center"</span>, <span class="st">"scale"</span>)</span>
<span id="cb29-26"><a href="#cb29-26" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb29-27"><a href="#cb29-27" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb29-28"><a href="#cb29-28" aria-hidden="true" tabindex="-1"></a>x_treino_norm <span class="ot">&lt;-</span> <span class="fu">predict</span>(preproc, x_treino)</span>
<span id="cb29-29"><a href="#cb29-29" aria-hidden="true" tabindex="-1"></a>x_teste_norm <span class="ot">&lt;-</span> <span class="fu">predict</span>(preproc, x_teste)</span>
<span id="cb29-30"><a href="#cb29-30" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb29-31"><a href="#cb29-31" aria-hidden="true" tabindex="-1"></a><span class="fu">cat</span>(<span class="st">"Preditores usados nos modelos:"</span>, <span class="fu">ncol</span>(x_treino_norm), <span class="st">"</span><span class="sc">\n</span><span class="st">"</span>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="cell-output cell-output-stdout">
<pre><code>Preditores usados nos modelos: 1262 </code></pre>
</div>
</div>
</section>
<section id="modelo-1-svm-linear" class="level1" data-number="10">
<h1 data-number="10"><span class="header-section-number">10</span> Modelo 1: SVM linear</h1>
<div class="cell">
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb31"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb31-1"><a href="#cb31-1" aria-hidden="true" tabindex="-1"></a><span class="co"># ============================================================</span></span>
<span id="cb31-2"><a href="#cb31-2" aria-hidden="true" tabindex="-1"></a><span class="co"># 10. SVM linear</span></span>
<span id="cb31-3"><a href="#cb31-3" aria-hidden="true" tabindex="-1"></a><span class="co"># ============================================================</span></span>
<span id="cb31-4"><a href="#cb31-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb31-5"><a href="#cb31-5" aria-hidden="true" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">123</span>)</span>
<span id="cb31-6"><a href="#cb31-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb31-7"><a href="#cb31-7" aria-hidden="true" tabindex="-1"></a>modelo_svm_linear <span class="ot">&lt;-</span> <span class="fu">svm</span>(</span>
<span id="cb31-8"><a href="#cb31-8" aria-hidden="true" tabindex="-1"></a> <span class="at">x =</span> x_treino_norm,</span>
<span id="cb31-9"><a href="#cb31-9" aria-hidden="true" tabindex="-1"></a> <span class="at">y =</span> y_treino,</span>
<span id="cb31-10"><a href="#cb31-10" aria-hidden="true" tabindex="-1"></a> <span class="at">kernel =</span> <span class="st">"linear"</span>,</span>
<span id="cb31-11"><a href="#cb31-11" aria-hidden="true" tabindex="-1"></a> <span class="at">cost =</span> <span class="dv">1</span>,</span>
<span id="cb31-12"><a href="#cb31-12" aria-hidden="true" tabindex="-1"></a> <span class="at">scale =</span> <span class="cn">FALSE</span></span>
<span id="cb31-13"><a href="#cb31-13" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb31-14"><a href="#cb31-14" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb31-15"><a href="#cb31-15" aria-hidden="true" tabindex="-1"></a>pred_svm_linear <span class="ot">&lt;-</span> <span class="fu">predict</span>(modelo_svm_linear, x_teste_norm)</span>
<span id="cb31-16"><a href="#cb31-16" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb31-17"><a href="#cb31-17" aria-hidden="true" tabindex="-1"></a>cm_svm_linear <span class="ot">&lt;-</span> <span class="fu">confusionMatrix</span>(pred_svm_linear, y_teste)</span>
<span id="cb31-18"><a href="#cb31-18" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb31-19"><a href="#cb31-19" aria-hidden="true" tabindex="-1"></a>cm_svm_linear</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="cell-output cell-output-stdout">
<pre><code>Confusion Matrix and Statistics
Reference
Prediction C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13
C1 12 4 1 1 1 3 1 0 8 1 1 1 1
C2 9 14 0 0 0 5 0 0 0 0 0 0 1
C3 0 1 10 1 0 0 0 2 4 0 0 0 5
C4 2 1 4 23 0 0 1 0 0 0 0 0 9
C5 0 0 5 0 21 0 0 0 0 0 0 0 2
C6 1 7 0 0 0 11 1 7 0 2 0 4 1
C7 0 0 2 1 0 1 11 7 0 0 0 8 2
C8 0 0 0 0 1 4 5 7 0 1 1 5 0
C9 2 1 3 0 0 1 0 1 13 0 0 3 1
C10 1 0 0 0 0 0 0 0 2 1 22 0 0
C11 1 0 1 1 0 1 0 0 1 22 3 0 0
C12 0 0 0 0 0 0 7 3 0 0 0 5 1
C13 0 0 2 1 5 2 2 1 0 1 1 2 5
Overall Statistics
Accuracy : 0.3736
95% CI : (0.3238, 0.4256)
No Information Rate : 0.0769
P-Value [Acc &gt; NIR] : &lt; 2.2e-16
Kappa : 0.3214
Mcnemar's Test P-Value : NA
Statistics by Class:
Class: C1 Class: C2 Class: C3 Class: C4 Class: C5
Sensitivity 0.42857 0.50000 0.35714 0.82143 0.75000
Specificity 0.93155 0.95536 0.96131 0.94940 0.97917
Pos Pred Value 0.34286 0.48276 0.43478 0.57500 0.75000
Neg Pred Value 0.95137 0.95821 0.94721 0.98457 0.97917
Prevalence 0.07692 0.07692 0.07692 0.07692 0.07692
Detection Rate 0.03297 0.03846 0.02747 0.06319 0.05769
Detection Prevalence 0.09615 0.07967 0.06319 0.10989 0.07692
Balanced Accuracy 0.68006 0.72768 0.65923 0.88542 0.86458
Class: C6 Class: C7 Class: C8 Class: C9 Class: C10
Sensitivity 0.39286 0.39286 0.25000 0.46429 0.035714
Specificity 0.93155 0.93750 0.94940 0.96429 0.925595
Pos Pred Value 0.32353 0.34375 0.29167 0.52000 0.038462
Neg Pred Value 0.94848 0.94880 0.93824 0.95575 0.920118
Prevalence 0.07692 0.07692 0.07692 0.07692 0.076923
Detection Rate 0.03022 0.03022 0.01923 0.03571 0.002747
Detection Prevalence 0.09341 0.08791 0.06593 0.06868 0.071429
Balanced Accuracy 0.66220 0.66518 0.59970 0.71429 0.480655
Class: C11 Class: C12 Class: C13
Sensitivity 0.107143 0.17857 0.17857
Specificity 0.919643 0.96726 0.94940
Pos Pred Value 0.100000 0.31250 0.22727
Neg Pred Value 0.925150 0.93391 0.93275
Prevalence 0.076923 0.07692 0.07692
Detection Rate 0.008242 0.01374 0.01374
Detection Prevalence 0.082418 0.04396 0.06044
Balanced Accuracy 0.513393 0.57292 0.56399</code></pre>
</div>
</div>
</section>
<section id="modelo-2-svm-radial" class="level1" data-number="11">
<h1 data-number="11"><span class="header-section-number">11</span> Modelo 2: SVM radial</h1>
<div class="cell">
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb33"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb33-1"><a href="#cb33-1" aria-hidden="true" tabindex="-1"></a><span class="co"># ============================================================</span></span>
<span id="cb33-2"><a href="#cb33-2" aria-hidden="true" tabindex="-1"></a><span class="co"># 11. SVM radial com parametros fixos</span></span>
<span id="cb33-3"><a href="#cb33-3" aria-hidden="true" tabindex="-1"></a><span class="co"># ============================================================</span></span>
<span id="cb33-4"><a href="#cb33-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb33-5"><a href="#cb33-5" aria-hidden="true" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">123</span>)</span>
<span id="cb33-6"><a href="#cb33-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb33-7"><a href="#cb33-7" aria-hidden="true" tabindex="-1"></a>modelo_svm_radial <span class="ot">&lt;-</span> <span class="fu">svm</span>(</span>
<span id="cb33-8"><a href="#cb33-8" aria-hidden="true" tabindex="-1"></a> <span class="at">x =</span> x_treino_norm,</span>
<span id="cb33-9"><a href="#cb33-9" aria-hidden="true" tabindex="-1"></a> <span class="at">y =</span> y_treino,</span>
<span id="cb33-10"><a href="#cb33-10" aria-hidden="true" tabindex="-1"></a> <span class="at">kernel =</span> <span class="st">"radial"</span>,</span>
<span id="cb33-11"><a href="#cb33-11" aria-hidden="true" tabindex="-1"></a> <span class="at">cost =</span> <span class="dv">10</span>,</span>
<span id="cb33-12"><a href="#cb33-12" aria-hidden="true" tabindex="-1"></a> <span class="at">gamma =</span> <span class="fl">0.01</span>,</span>
<span id="cb33-13"><a href="#cb33-13" aria-hidden="true" tabindex="-1"></a> <span class="at">scale =</span> <span class="cn">FALSE</span></span>
<span id="cb33-14"><a href="#cb33-14" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb33-15"><a href="#cb33-15" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb33-16"><a href="#cb33-16" aria-hidden="true" tabindex="-1"></a>pred_svm_radial <span class="ot">&lt;-</span> <span class="fu">predict</span>(modelo_svm_radial, x_teste_norm)</span>
<span id="cb33-17"><a href="#cb33-17" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb33-18"><a href="#cb33-18" aria-hidden="true" tabindex="-1"></a>cm_svm_radial <span class="ot">&lt;-</span> <span class="fu">confusionMatrix</span>(pred_svm_radial, y_teste)</span>
<span id="cb33-19"><a href="#cb33-19" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb33-20"><a href="#cb33-20" aria-hidden="true" tabindex="-1"></a>cm_svm_radial</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="cell-output cell-output-stdout">
<pre><code>Confusion Matrix and Statistics
Reference
Prediction C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13
C1 4 4 0 0 0 2 0 0 5 2 2 0 0
C2 7 2 0 0 0 5 0 0 0 0 0 0 1
C3 0 0 2 1 0 0 0 2 4 0 0 0 2
C4 1 0 4 5 0 0 1 0 0 0 0 0 3
C5 0 0 3 0 8 0 0 0 0 0 0 0 2
C6 1 6 0 0 0 8 0 6 0 2 0 1 1
C7 0 0 0 0 0 1 0 4 0 0 0 6 1
C8 0 0 0 0 0 1 6 2 0 1 1 6 0
C9 0 0 3 0 0 0 2 3 4 0 0 2 0
C10 1 0 0 0 0 0 0 0 0 1 22 0 0
C11 1 0 1 0 0 1 0 0 1 19 0 0 0
C12 0 0 0 0 0 1 6 3 0 0 0 1 1
C13 13 16 15 22 20 9 13 8 14 3 3 12 17
Overall Statistics
Accuracy : 0.1484
95% CI : (0.1135, 0.1891)
No Information Rate : 0.0769
P-Value [Acc &gt; NIR] : 3.007e-06
Kappa : 0.0774
Mcnemar's Test P-Value : NA
Statistics by Class:
Class: C1 Class: C2 Class: C3 Class: C4 Class: C5
Sensitivity 0.14286 0.071429 0.071429 0.17857 0.28571
Specificity 0.95536 0.961310 0.973214 0.97321 0.98512
Pos Pred Value 0.21053 0.133333 0.181818 0.35714 0.61538
Neg Pred Value 0.93043 0.925501 0.926346 0.93429 0.94302
Prevalence 0.07692 0.076923 0.076923 0.07692 0.07692
Detection Rate 0.01099 0.005495 0.005495 0.01374 0.02198
Detection Prevalence 0.05220 0.041209 0.030220 0.03846 0.03571
Balanced Accuracy 0.54911 0.516369 0.522321 0.57589 0.63542
Class: C6 Class: C7 Class: C8 Class: C9 Class: C10
Sensitivity 0.28571 0.00000 0.071429 0.14286 0.035714
Specificity 0.94940 0.96429 0.955357 0.97024 0.931548
Pos Pred Value 0.32000 0.00000 0.117647 0.28571 0.041667
Neg Pred Value 0.94100 0.92045 0.925072 0.93143 0.920588
Prevalence 0.07692 0.07692 0.076923 0.07692 0.076923
Detection Rate 0.02198 0.00000 0.005495 0.01099 0.002747
Detection Prevalence 0.06868 0.03297 0.046703 0.03846 0.065934
Balanced Accuracy 0.61756 0.48214 0.513393 0.55655 0.483631
Class: C11 Class: C12 Class: C13
Sensitivity 0.00000 0.035714 0.60714
Specificity 0.93155 0.967262 0.55952
Pos Pred Value 0.00000 0.083333 0.10303
Neg Pred Value 0.91789 0.923295 0.94472
Prevalence 0.07692 0.076923 0.07692
Detection Rate 0.00000 0.002747 0.04670
Detection Prevalence 0.06319 0.032967 0.45330
Balanced Accuracy 0.46577 0.501488 0.58333</code></pre>
</div>
</div>
</section>
<section id="modelo-3-ajuste-de-hiperparametros-do-svm-radial" class="level1" data-number="12">
<h1 data-number="12"><span class="header-section-number">12</span> Modelo 3: Ajuste de hiperparametros do SVM radial</h1>
<div class="cell">
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb35"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb35-1"><a href="#cb35-1" aria-hidden="true" tabindex="-1"></a><span class="co"># ============================================================</span></span>
<span id="cb35-2"><a href="#cb35-2" aria-hidden="true" tabindex="-1"></a><span class="co"># 12. Ajuste simples de hiperparametros do SVM radial</span></span>
<span id="cb35-3"><a href="#cb35-3" aria-hidden="true" tabindex="-1"></a><span class="co"># ============================================================</span></span>
<span id="cb35-4"><a href="#cb35-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb35-5"><a href="#cb35-5" aria-hidden="true" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">123</span>)</span>
<span id="cb35-6"><a href="#cb35-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb35-7"><a href="#cb35-7" aria-hidden="true" tabindex="-1"></a>k_cv <span class="ot">&lt;-</span> <span class="fu">min</span>(<span class="dv">5</span>, <span class="fu">as.integer</span>(<span class="fu">min</span>(<span class="fu">table</span>(y_treino))))</span>
<span id="cb35-8"><a href="#cb35-8" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb35-9"><a href="#cb35-9" aria-hidden="true" tabindex="-1"></a><span class="cf">if</span> (k_cv <span class="sc">&gt;=</span> <span class="dv">2</span>) {</span>
<span id="cb35-10"><a href="#cb35-10" aria-hidden="true" tabindex="-1"></a> ajuste_svm_radial <span class="ot">&lt;-</span> <span class="fu">tune.svm</span>(</span>
<span id="cb35-11"><a href="#cb35-11" aria-hidden="true" tabindex="-1"></a> <span class="at">x =</span> x_treino_norm,</span>
<span id="cb35-12"><a href="#cb35-12" aria-hidden="true" tabindex="-1"></a> <span class="at">y =</span> y_treino,</span>
<span id="cb35-13"><a href="#cb35-13" aria-hidden="true" tabindex="-1"></a> <span class="at">kernel =</span> <span class="st">"radial"</span>,</span>
<span id="cb35-14"><a href="#cb35-14" aria-hidden="true" tabindex="-1"></a> <span class="at">cost =</span> <span class="fu">c</span>(<span class="fl">0.1</span>, <span class="dv">1</span>, <span class="dv">10</span>, <span class="dv">100</span>),</span>
<span id="cb35-15"><a href="#cb35-15" aria-hidden="true" tabindex="-1"></a> <span class="at">gamma =</span> <span class="fu">c</span>(<span class="fl">0.001</span>, <span class="fl">0.01</span>, <span class="fl">0.05</span>, <span class="fl">0.1</span>),</span>
<span id="cb35-16"><a href="#cb35-16" aria-hidden="true" tabindex="-1"></a> <span class="at">tunecontrol =</span> <span class="fu">tune.control</span>(<span class="at">cross =</span> k_cv)</span>
<span id="cb35-17"><a href="#cb35-17" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb35-18"><a href="#cb35-18" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb35-19"><a href="#cb35-19" aria-hidden="true" tabindex="-1"></a> modelo_svm_radial_ajustado <span class="ot">&lt;-</span> ajuste_svm_radial<span class="sc">$</span>best.model</span>
<span id="cb35-20"><a href="#cb35-20" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb35-21"><a href="#cb35-21" aria-hidden="true" tabindex="-1"></a> pred_svm_radial_ajustado <span class="ot">&lt;-</span> <span class="fu">predict</span>(</span>
<span id="cb35-22"><a href="#cb35-22" aria-hidden="true" tabindex="-1"></a> modelo_svm_radial_ajustado,</span>
<span id="cb35-23"><a href="#cb35-23" aria-hidden="true" tabindex="-1"></a> x_teste_norm</span>
<span id="cb35-24"><a href="#cb35-24" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb35-25"><a href="#cb35-25" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb35-26"><a href="#cb35-26" aria-hidden="true" tabindex="-1"></a> cm_svm_radial_ajustado <span class="ot">&lt;-</span> <span class="fu">confusionMatrix</span>(</span>
<span id="cb35-27"><a href="#cb35-27" aria-hidden="true" tabindex="-1"></a> pred_svm_radial_ajustado,</span>
<span id="cb35-28"><a href="#cb35-28" aria-hidden="true" tabindex="-1"></a> y_teste</span>
<span id="cb35-29"><a href="#cb35-29" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb35-30"><a href="#cb35-30" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb35-31"><a href="#cb35-31" aria-hidden="true" tabindex="-1"></a> ajuste_svm_radial<span class="sc">$</span>best.parameters <span class="sc">%&gt;%</span></span>
<span id="cb35-32"><a href="#cb35-32" aria-hidden="true" tabindex="-1"></a> <span class="fu">kable</span>()</span>
<span id="cb35-33"><a href="#cb35-33" aria-hidden="true" tabindex="-1"></a>} <span class="cf">else</span> {</span>
<span id="cb35-34"><a href="#cb35-34" aria-hidden="true" tabindex="-1"></a> <span class="fu">warning</span>(<span class="st">"Poucas amostras por classe para validacao cruzada."</span>)</span>
<span id="cb35-35"><a href="#cb35-35" aria-hidden="true" tabindex="-1"></a> ajuste_svm_radial <span class="ot">&lt;-</span> <span class="cn">NULL</span></span>
<span id="cb35-36"><a href="#cb35-36" aria-hidden="true" tabindex="-1"></a> modelo_svm_radial_ajustado <span class="ot">&lt;-</span> modelo_svm_radial</span>
<span id="cb35-37"><a href="#cb35-37" aria-hidden="true" tabindex="-1"></a> pred_svm_radial_ajustado <span class="ot">&lt;-</span> pred_svm_radial</span>
<span id="cb35-38"><a href="#cb35-38" aria-hidden="true" tabindex="-1"></a> cm_svm_radial_ajustado <span class="ot">&lt;-</span> cm_svm_radial</span>
<span id="cb35-39"><a href="#cb35-39" aria-hidden="true" tabindex="-1"></a>}</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="cell-output-display">
<table class="caption-top table table-sm table-striped small">
<thead>
<tr class="header">
<th style="text-align: left;"></th>
<th style="text-align: right;">gamma</th>
<th style="text-align: right;">cost</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">9</td>
<td style="text-align: right;">0.001</td>
<td style="text-align: right;">10</td>
</tr>
</tbody>
</table>
</div>
</div>
<div class="cell">
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb36"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb36-1"><a href="#cb36-1" aria-hidden="true" tabindex="-1"></a>cm_svm_radial_ajustado</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="cell-output cell-output-stdout">
<pre><code>Confusion Matrix and Statistics
Reference
Prediction C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13
C1 12 4 0 2 0 3 0 0 8 1 1 1 1
C2 9 14 0 0 0 5 0 0 0 0 0 0 1
C3 0 1 10 1 0 0 0 2 4 0 0 0 3
C4 2 1 5 20 0 0 2 0 0 0 0 0 8
C5 0 0 5 0 18 0 0 0 0 0 0 0 1
C6 1 7 0 0 0 10 0 8 0 1 0 4 2
C7 0 0 1 2 0 1 9 5 0 0 0 8 2
C8 0 0 0 0 1 4 7 8 0 1 1 5 1
C9 1 1 3 0 0 2 0 1 15 0 0 2 0
C10 1 0 0 0 0 0 0 0 0 1 22 0 0
C11 2 0 1 1 0 1 0 0 1 23 3 0 0
C12 0 0 1 1 0 0 7 3 0 0 0 5 1
C13 0 0 2 1 9 2 3 1 0 1 1 3 8
Overall Statistics
Accuracy : 0.3654
95% CI : (0.3158, 0.4172)
No Information Rate : 0.0769
P-Value [Acc &gt; NIR] : &lt; 2.2e-16
Kappa : 0.3125
Mcnemar's Test P-Value : NA
Statistics by Class:
Class: C1 Class: C2 Class: C3 Class: C4 Class: C5
Sensitivity 0.42857 0.50000 0.35714 0.71429 0.64286
Specificity 0.93750 0.95536 0.96726 0.94643 0.98214
Pos Pred Value 0.36364 0.48276 0.47619 0.52632 0.75000
Neg Pred Value 0.95166 0.95821 0.94752 0.97546 0.97059
Prevalence 0.07692 0.07692 0.07692 0.07692 0.07692
Detection Rate 0.03297 0.03846 0.02747 0.05495 0.04945
Detection Prevalence 0.09066 0.07967 0.05769 0.10440 0.06593
Balanced Accuracy 0.68304 0.72768 0.66220 0.83036 0.81250
Class: C6 Class: C7 Class: C8 Class: C9 Class: C10
Sensitivity 0.35714 0.32143 0.28571 0.53571 0.035714
Specificity 0.93155 0.94345 0.94048 0.97024 0.931548
Pos Pred Value 0.30303 0.32143 0.28571 0.60000 0.041667
Neg Pred Value 0.94562 0.94345 0.94048 0.96165 0.920588
Prevalence 0.07692 0.07692 0.07692 0.07692 0.076923
Detection Rate 0.02747 0.02473 0.02198 0.04121 0.002747
Detection Prevalence 0.09066 0.07692 0.07692 0.06868 0.065934
Balanced Accuracy 0.64435 0.63244 0.61310 0.75298 0.483631
Class: C11 Class: C12 Class: C13
Sensitivity 0.107143 0.17857 0.28571
Specificity 0.913690 0.96131 0.93155
Pos Pred Value 0.093750 0.27778 0.25806
Neg Pred Value 0.924699 0.93353 0.93994
Prevalence 0.076923 0.07692 0.07692
Detection Rate 0.008242 0.01374 0.02198
Detection Prevalence 0.087912 0.04945 0.08516
Balanced Accuracy 0.510417 0.56994 0.60863</code></pre>
</div>
</div>
</section>
<section id="modelo-4-pca-svm-radial" class="level1" data-number="13">
<h1 data-number="13"><span class="header-section-number">13</span> Modelo 4: PCA + SVM radial</h1>
<div class="cell">
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb38"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb38-1"><a href="#cb38-1" aria-hidden="true" tabindex="-1"></a><span class="co"># ============================================================</span></span>
<span id="cb38-2"><a href="#cb38-2" aria-hidden="true" tabindex="-1"></a><span class="co"># 13. PCA</span></span>
<span id="cb38-3"><a href="#cb38-3" aria-hidden="true" tabindex="-1"></a><span class="co"># ============================================================</span></span>
<span id="cb38-4"><a href="#cb38-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb38-5"><a href="#cb38-5" aria-hidden="true" tabindex="-1"></a>pca <span class="ot">&lt;-</span> <span class="fu">prcomp</span>(</span>
<span id="cb38-6"><a href="#cb38-6" aria-hidden="true" tabindex="-1"></a> x_treino_norm,</span>
<span id="cb38-7"><a href="#cb38-7" aria-hidden="true" tabindex="-1"></a> <span class="at">center =</span> <span class="cn">FALSE</span>,</span>
<span id="cb38-8"><a href="#cb38-8" aria-hidden="true" tabindex="-1"></a> <span class="at">scale. =</span> <span class="cn">FALSE</span></span>
<span id="cb38-9"><a href="#cb38-9" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb38-10"><a href="#cb38-10" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb38-11"><a href="#cb38-11" aria-hidden="true" tabindex="-1"></a>variancia <span class="ot">&lt;-</span> pca<span class="sc">$</span>sdev<span class="sc">^</span><span class="dv">2</span></span>
<span id="cb38-12"><a href="#cb38-12" aria-hidden="true" tabindex="-1"></a>variancia_exp <span class="ot">&lt;-</span> variancia <span class="sc">/</span> <span class="fu">sum</span>(variancia)</span>
<span id="cb38-13"><a href="#cb38-13" aria-hidden="true" tabindex="-1"></a>variancia_acum <span class="ot">&lt;-</span> <span class="fu">cumsum</span>(variancia_exp)</span>
<span id="cb38-14"><a href="#cb38-14" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb38-15"><a href="#cb38-15" aria-hidden="true" tabindex="-1"></a>n_comp_95 <span class="ot">&lt;-</span> <span class="fu">which</span>(variancia_acum <span class="sc">&gt;=</span> <span class="fl">0.95</span>)[<span class="dv">1</span>]</span>
<span id="cb38-16"><a href="#cb38-16" aria-hidden="true" tabindex="-1"></a>n_comp <span class="ot">&lt;-</span> <span class="fu">min</span>(n_comp_95, <span class="dv">30</span>, <span class="fu">ncol</span>(x_treino_norm))</span>
<span id="cb38-17"><a href="#cb38-17" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb38-18"><a href="#cb38-18" aria-hidden="true" tabindex="-1"></a><span class="fu">cat</span>(<span class="st">"Componentes necessarios para 95% da variancia:"</span>, n_comp_95, <span class="st">"</span><span class="sc">\n</span><span class="st">"</span>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="cell-output cell-output-stdout">
<pre><code>Componentes necessarios para 95% da variancia: 364 </code></pre>
</div>
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb40"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb40-1"><a href="#cb40-1" aria-hidden="true" tabindex="-1"></a><span class="fu">cat</span>(<span class="st">"Componentes usados no modelo:"</span>, n_comp, <span class="st">"</span><span class="sc">\n</span><span class="st">"</span>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="cell-output cell-output-stdout">
<pre><code>Componentes usados no modelo: 30 </code></pre>
</div>
</div>
<div class="cell">
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb42"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb42-1"><a href="#cb42-1" aria-hidden="true" tabindex="-1"></a><span class="fu">tibble</span>(</span>
<span id="cb42-2"><a href="#cb42-2" aria-hidden="true" tabindex="-1"></a> <span class="at">componente =</span> <span class="fu">seq_along</span>(variancia_acum),</span>
<span id="cb42-3"><a href="#cb42-3" aria-hidden="true" tabindex="-1"></a> <span class="at">variancia_acumulada =</span> variancia_acum</span>
<span id="cb42-4"><a href="#cb42-4" aria-hidden="true" tabindex="-1"></a>) <span class="sc">%&gt;%</span></span>
<span id="cb42-5"><a href="#cb42-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">ggplot</span>(<span class="fu">aes</span>(<span class="at">x =</span> componente, <span class="at">y =</span> variancia_acumulada)) <span class="sc">+</span></span>
<span id="cb42-6"><a href="#cb42-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_line</span>(<span class="at">color =</span> <span class="st">"#2C7FB8"</span>) <span class="sc">+</span></span>
<span id="cb42-7"><a href="#cb42-7" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_point</span>(<span class="at">color =</span> <span class="st">"#2C7FB8"</span>) <span class="sc">+</span></span>
<span id="cb42-8"><a href="#cb42-8" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_hline</span>(<span class="at">yintercept =</span> <span class="fl">0.95</span>, <span class="at">linetype =</span> <span class="st">"dashed"</span>) <span class="sc">+</span></span>
<span id="cb42-9"><a href="#cb42-9" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
<span id="cb42-10"><a href="#cb42-10" aria-hidden="true" tabindex="-1"></a> <span class="at">title =</span> <span class="st">"Variancia acumulada pelo PCA (dataset completo)"</span>,</span>
<span id="cb42-11"><a href="#cb42-11" aria-hidden="true" tabindex="-1"></a> <span class="at">x =</span> <span class="st">"Numero de componentes principais"</span>,</span>
<span id="cb42-12"><a href="#cb42-12" aria-hidden="true" tabindex="-1"></a> <span class="at">y =</span> <span class="st">"Variancia acumulada"</span></span>
<span id="cb42-13"><a href="#cb42-13" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">+</span></span>
<span id="cb42-14"><a href="#cb42-14" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme_minimal</span>()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="trabalho_svm_rgb_completo_files/figure-html/unnamed-chunk-16-1.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
</div>
<div class="cell">
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb43"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb43-1"><a href="#cb43-1" aria-hidden="true" tabindex="-1"></a>x_treino_pca <span class="ot">&lt;-</span> <span class="fu">as.data.frame</span>(pca<span class="sc">$</span>x[, <span class="dv">1</span><span class="sc">:</span>n_comp, <span class="at">drop =</span> <span class="cn">FALSE</span>])</span>
<span id="cb43-2"><a href="#cb43-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb43-3"><a href="#cb43-3" aria-hidden="true" tabindex="-1"></a>x_teste_pca <span class="ot">&lt;-</span> <span class="fu">as.data.frame</span>(</span>
<span id="cb43-4"><a href="#cb43-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">predict</span>(pca, <span class="at">newdata =</span> x_teste_norm)[, <span class="dv">1</span><span class="sc">:</span>n_comp, <span class="at">drop =</span> <span class="cn">FALSE</span>]</span>
<span id="cb43-5"><a href="#cb43-5" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb43-6"><a href="#cb43-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb43-7"><a href="#cb43-7" aria-hidden="true" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">123</span>)</span>
<span id="cb43-8"><a href="#cb43-8" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb43-9"><a href="#cb43-9" aria-hidden="true" tabindex="-1"></a>modelo_svm_pca <span class="ot">&lt;-</span> <span class="fu">svm</span>(</span>
<span id="cb43-10"><a href="#cb43-10" aria-hidden="true" tabindex="-1"></a> <span class="at">x =</span> x_treino_pca,</span>
<span id="cb43-11"><a href="#cb43-11" aria-hidden="true" tabindex="-1"></a> <span class="at">y =</span> y_treino,</span>
<span id="cb43-12"><a href="#cb43-12" aria-hidden="true" tabindex="-1"></a> <span class="at">kernel =</span> <span class="st">"radial"</span>,</span>
<span id="cb43-13"><a href="#cb43-13" aria-hidden="true" tabindex="-1"></a> <span class="at">cost =</span> <span class="dv">10</span>,</span>
<span id="cb43-14"><a href="#cb43-14" aria-hidden="true" tabindex="-1"></a> <span class="at">gamma =</span> <span class="fl">0.01</span>,</span>
<span id="cb43-15"><a href="#cb43-15" aria-hidden="true" tabindex="-1"></a> <span class="at">scale =</span> <span class="cn">FALSE</span></span>
<span id="cb43-16"><a href="#cb43-16" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb43-17"><a href="#cb43-17" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb43-18"><a href="#cb43-18" aria-hidden="true" tabindex="-1"></a>pred_svm_pca <span class="ot">&lt;-</span> <span class="fu">predict</span>(modelo_svm_pca, x_teste_pca)</span>
<span id="cb43-19"><a href="#cb43-19" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb43-20"><a href="#cb43-20" aria-hidden="true" tabindex="-1"></a>cm_svm_pca <span class="ot">&lt;-</span> <span class="fu">confusionMatrix</span>(pred_svm_pca, y_teste)</span>
<span id="cb43-21"><a href="#cb43-21" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb43-22"><a href="#cb43-22" aria-hidden="true" tabindex="-1"></a>cm_svm_pca</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="cell-output cell-output-stdout">
<pre><code>Confusion Matrix and Statistics
Reference
Prediction C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13
C1 9 4 0 2 0 5 1 0 6 1 1 0 2
C2 10 10 0 0 0 5 0 0 0 0 0 0 2
C3 0 0 9 1 0 0 0 3 4 0 0 0 2
C4 2 1 5 15 0 0 1 0 0 0 0 1 6
C5 0 1 5 0 14 0 0 0 0 0 0 0 1
C6 1 6 0 0 0 9 1 7 0 1 0 1 0
C7 1 0 0 4 0 1 9 5 0 0 0 8 3
C8 0 0 0 0 0 1 6 5 0 1 0 5 1
C9 1 1 3 0 0 1 1 3 13 0 0 3 0
C10 1 0 0 0 0 0 0 0 1 3 24 0 0
C11 1 1 1 1 1 3 0 0 1 20 1 0 0
C12 1 0 0 1 0 1 6 4 0 0 0 7 2
C13 1 4 5 4 13 2 3 1 3 2 2 3 9
Overall Statistics
Accuracy : 0.3104
95% CI : (0.2632, 0.3607)
No Information Rate : 0.0769
P-Value [Acc &gt; NIR] : &lt; 2.2e-16
Kappa : 0.253
Mcnemar's Test P-Value : NA
Statistics by Class:
Class: C1 Class: C2 Class: C3 Class: C4 Class: C5
Sensitivity 0.32143 0.35714 0.32143 0.53571 0.50000
Specificity 0.93452 0.94940 0.97024 0.95238 0.97917
Pos Pred Value 0.29032 0.37037 0.47368 0.48387 0.66667
Neg Pred Value 0.94294 0.94659 0.94493 0.96096 0.95918
Prevalence 0.07692 0.07692 0.07692 0.07692 0.07692
Detection Rate 0.02473 0.02747 0.02473 0.04121 0.03846
Detection Prevalence 0.08516 0.07418 0.05220 0.08516 0.05769
Balanced Accuracy 0.62798 0.65327 0.64583 0.74405 0.73958
Class: C6 Class: C7 Class: C8 Class: C9 Class: C10
Sensitivity 0.32143 0.32143 0.17857 0.46429 0.107143
Specificity 0.94940 0.93452 0.95833 0.96131 0.922619
Pos Pred Value 0.34615 0.29032 0.26316 0.50000 0.103448
Neg Pred Value 0.94379 0.94294 0.93333 0.95562 0.925373
Prevalence 0.07692 0.07692 0.07692 0.07692 0.076923
Detection Rate 0.02473 0.02473 0.01374 0.03571 0.008242
Detection Prevalence 0.07143 0.08516 0.05220 0.07143 0.079670
Balanced Accuracy 0.63542 0.62798 0.56845 0.71280 0.514881
Class: C11 Class: C12 Class: C13
Sensitivity 0.035714 0.25000 0.32143
Specificity 0.913690 0.95536 0.87202
Pos Pred Value 0.033333 0.31818 0.17308
Neg Pred Value 0.919162 0.93860 0.93910
Prevalence 0.076923 0.07692 0.07692
Detection Rate 0.002747 0.01923 0.02473
Detection Prevalence 0.082418 0.06044 0.14286
Balanced Accuracy 0.474702 0.60268 0.59673</code></pre>
</div>
</div>
</section>
<section id="modelo-5-random-forest" class="level1" data-number="14">
<h1 data-number="14"><span class="header-section-number">14</span> Modelo 5: Random Forest</h1>
<div class="cell">
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb45"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb45-1"><a href="#cb45-1" aria-hidden="true" tabindex="-1"></a><span class="co"># ============================================================</span></span>
<span id="cb45-2"><a href="#cb45-2" aria-hidden="true" tabindex="-1"></a><span class="co"># 14. Random Forest</span></span>
<span id="cb45-3"><a href="#cb45-3" aria-hidden="true" tabindex="-1"></a><span class="co"># ============================================================</span></span>
<span id="cb45-4"><a href="#cb45-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb45-5"><a href="#cb45-5" aria-hidden="true" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">123</span>)</span>
<span id="cb45-6"><a href="#cb45-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb45-7"><a href="#cb45-7" aria-hidden="true" tabindex="-1"></a>modelo_rf <span class="ot">&lt;-</span> <span class="fu">randomForest</span>(</span>
<span id="cb45-8"><a href="#cb45-8" aria-hidden="true" tabindex="-1"></a> <span class="at">x =</span> x_treino_norm,</span>
<span id="cb45-9"><a href="#cb45-9" aria-hidden="true" tabindex="-1"></a> <span class="at">y =</span> y_treino,</span>
<span id="cb45-10"><a href="#cb45-10" aria-hidden="true" tabindex="-1"></a> <span class="at">ntree =</span> <span class="dv">500</span>,</span>
<span id="cb45-11"><a href="#cb45-11" aria-hidden="true" tabindex="-1"></a> <span class="at">importance =</span> <span class="cn">TRUE</span></span>
<span id="cb45-12"><a href="#cb45-12" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb45-13"><a href="#cb45-13" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb45-14"><a href="#cb45-14" aria-hidden="true" tabindex="-1"></a>pred_rf <span class="ot">&lt;-</span> <span class="fu">predict</span>(modelo_rf, x_teste_norm)</span>
<span id="cb45-15"><a href="#cb45-15" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb45-16"><a href="#cb45-16" aria-hidden="true" tabindex="-1"></a>cm_rf <span class="ot">&lt;-</span> <span class="fu">confusionMatrix</span>(pred_rf, y_teste)</span>
<span id="cb45-17"><a href="#cb45-17" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb45-18"><a href="#cb45-18" aria-hidden="true" tabindex="-1"></a>cm_rf</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="cell-output cell-output-stdout">
<pre><code>Confusion Matrix and Statistics
Reference
Prediction C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13
C1 9 4 1 3 0 3 0 0 8 2 2 0 3
C2 9 10 0 0 0 5 0 0 0 0 0 0 1
C3 0 1 10 1 0 0 0 2 4 0 0 0 2
C4 5 2 5 18 0 0 2 0 0 0 0 0 6
C5 0 0 5 0 22 0 0 0 0 0 0 0 4
C6 1 6 0 0 0 11 0 9 0 1 0 4 2
C7 0 0 1 3 0 1 7 4 0 0 0 7 3
C8 0 1 0 0 0 1 8 7 1 1 0 4 1
C9 1 3 1 0 0 0 0 0 13 0 0 2 0
C10 1 0 2 1 0 1 0 1 0 3 24 0 0
C11 2 1 1 0 1 2 0 0 2 20 1 0 0
C12 0 0 0 1 1 2 8 4 0 0 0 8 1
C13 0 0 2 1 4 2 3 1 0 1 1 3 5
Overall Statistics
Accuracy : 0.3407
95% CI : (0.2921, 0.3919)
No Information Rate : 0.0769
P-Value [Acc &gt; NIR] : &lt; 2.2e-16
Kappa : 0.2857
Mcnemar's Test P-Value : NA
Statistics by Class:
Class: C1 Class: C2 Class: C3 Class: C4 Class: C5
Sensitivity 0.32143 0.35714 0.35714 0.64286 0.78571
Specificity 0.92262 0.95536 0.97024 0.94048 0.97321
Pos Pred Value 0.25714 0.40000 0.50000 0.47368 0.70968
Neg Pred Value 0.94225 0.94690 0.94767 0.96933 0.98198
Prevalence 0.07692 0.07692 0.07692 0.07692 0.07692
Detection Rate 0.02473 0.02747 0.02747 0.04945 0.06044
Detection Prevalence 0.09615 0.06868 0.05495 0.10440 0.08516
Balanced Accuracy 0.62202 0.65625 0.66369 0.79167 0.87946
Class: C6 Class: C7 Class: C8 Class: C9 Class: C10
Sensitivity 0.39286 0.25000 0.25000 0.46429 0.107143
Specificity 0.93155 0.94345 0.94940 0.97917 0.910714
Pos Pred Value 0.32353 0.26923 0.29167 0.65000 0.090909
Neg Pred Value 0.94848 0.93787 0.93824 0.95640 0.924471
Prevalence 0.07692 0.07692 0.07692 0.07692 0.076923
Detection Rate 0.03022 0.01923 0.01923 0.03571 0.008242
Detection Prevalence 0.09341 0.07143 0.06593 0.05495 0.090659
Balanced Accuracy 0.66220 0.59673 0.59970 0.72173 0.508929
Class: C11 Class: C12 Class: C13
Sensitivity 0.035714 0.28571 0.17857
Specificity 0.913690 0.94940 0.94643
Pos Pred Value 0.033333 0.32000 0.21739
Neg Pred Value 0.919162 0.94100 0.93255
Prevalence 0.076923 0.07692 0.07692
Detection Rate 0.002747 0.02198 0.01374
Detection Prevalence 0.082418 0.06868 0.06319
Balanced Accuracy 0.474702 0.61756 0.56250</code></pre>
</div>
</div>
</section>
<section id="comparacao-dos-modelos" class="level1" data-number="15">
<h1 data-number="15"><span class="header-section-number">15</span> Comparacao dos modelos</h1>
<div class="cell">
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb47"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb47-1"><a href="#cb47-1" aria-hidden="true" tabindex="-1"></a><span class="co"># ============================================================</span></span>
<span id="cb47-2"><a href="#cb47-2" aria-hidden="true" tabindex="-1"></a><span class="co"># 15. Funcao de avaliacao dos modelos</span></span>
<span id="cb47-3"><a href="#cb47-3" aria-hidden="true" tabindex="-1"></a><span class="co"># ============================================================</span></span>
<span id="cb47-4"><a href="#cb47-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb47-5"><a href="#cb47-5" aria-hidden="true" tabindex="-1"></a>avaliar_modelo <span class="ot">&lt;-</span> <span class="cf">function</span>(nome, matriz_confusao) {</span>
<span id="cb47-6"><a href="#cb47-6" aria-hidden="true" tabindex="-1"></a> overall <span class="ot">&lt;-</span> matriz_confusao<span class="sc">$</span>overall</span>
<span id="cb47-7"><a href="#cb47-7" aria-hidden="true" tabindex="-1"></a> by_class <span class="ot">&lt;-</span> matriz_confusao<span class="sc">$</span>byClass</span>
<span id="cb47-8"><a href="#cb47-8" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb47-9"><a href="#cb47-9" aria-hidden="true" tabindex="-1"></a> <span class="cf">if</span> (<span class="fu">is.matrix</span>(by_class)) {</span>
<span id="cb47-10"><a href="#cb47-10" aria-hidden="true" tabindex="-1"></a> sensibilidade_macro <span class="ot">&lt;-</span> <span class="fu">mean</span>(by_class[, <span class="st">"Sensitivity"</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>)</span>
<span id="cb47-11"><a href="#cb47-11" aria-hidden="true" tabindex="-1"></a> especificidade_macro <span class="ot">&lt;-</span> <span class="fu">mean</span>(by_class[, <span class="st">"Specificity"</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>)</span>
<span id="cb47-12"><a href="#cb47-12" aria-hidden="true" tabindex="-1"></a> f1_macro <span class="ot">&lt;-</span> <span class="fu">mean</span>(by_class[, <span class="st">"F1"</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>)</span>
<span id="cb47-13"><a href="#cb47-13" aria-hidden="true" tabindex="-1"></a> } <span class="cf">else</span> {</span>
<span id="cb47-14"><a href="#cb47-14" aria-hidden="true" tabindex="-1"></a> sensibilidade_macro <span class="ot">&lt;-</span> by_class[<span class="st">"Sensitivity"</span>]</span>
<span id="cb47-15"><a href="#cb47-15" aria-hidden="true" tabindex="-1"></a> especificidade_macro <span class="ot">&lt;-</span> by_class[<span class="st">"Specificity"</span>]</span>
<span id="cb47-16"><a href="#cb47-16" aria-hidden="true" tabindex="-1"></a> f1_macro <span class="ot">&lt;-</span> by_class[<span class="st">"F1"</span>]</span>
<span id="cb47-17"><a href="#cb47-17" aria-hidden="true" tabindex="-1"></a> }</span>
<span id="cb47-18"><a href="#cb47-18" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb47-19"><a href="#cb47-19" aria-hidden="true" tabindex="-1"></a> <span class="fu">tibble</span>(</span>
<span id="cb47-20"><a href="#cb47-20" aria-hidden="true" tabindex="-1"></a> <span class="at">modelo =</span> nome,</span>
<span id="cb47-21"><a href="#cb47-21" aria-hidden="true" tabindex="-1"></a> <span class="at">acuracia =</span> <span class="fu">as.numeric</span>(overall[<span class="st">"Accuracy"</span>]),</span>
<span id="cb47-22"><a href="#cb47-22" aria-hidden="true" tabindex="-1"></a> <span class="at">kappa =</span> <span class="fu">as.numeric</span>(overall[<span class="st">"Kappa"</span>]),</span>
<span id="cb47-23"><a href="#cb47-23" aria-hidden="true" tabindex="-1"></a> <span class="at">sensibilidade_macro =</span> <span class="fu">as.numeric</span>(sensibilidade_macro),</span>
<span id="cb47-24"><a href="#cb47-24" aria-hidden="true" tabindex="-1"></a> <span class="at">especificidade_macro =</span> <span class="fu">as.numeric</span>(especificidade_macro),</span>
<span id="cb47-25"><a href="#cb47-25" aria-hidden="true" tabindex="-1"></a> <span class="at">f1_macro =</span> <span class="fu">as.numeric</span>(f1_macro)</span>
<span id="cb47-26"><a href="#cb47-26" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb47-27"><a href="#cb47-27" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb47-28"><a href="#cb47-28" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb47-29"><a href="#cb47-29" aria-hidden="true" tabindex="-1"></a>resultados <span class="ot">&lt;-</span> <span class="fu">bind_rows</span>(</span>
<span id="cb47-30"><a href="#cb47-30" aria-hidden="true" tabindex="-1"></a> <span class="fu">avaliar_modelo</span>(<span class="st">"SVM linear"</span>, cm_svm_linear),</span>
<span id="cb47-31"><a href="#cb47-31" aria-hidden="true" tabindex="-1"></a> <span class="fu">avaliar_modelo</span>(<span class="st">"SVM radial"</span>, cm_svm_radial),</span>
<span id="cb47-32"><a href="#cb47-32" aria-hidden="true" tabindex="-1"></a> <span class="fu">avaliar_modelo</span>(<span class="st">"SVM radial ajustado"</span>, cm_svm_radial_ajustado),</span>
<span id="cb47-33"><a href="#cb47-33" aria-hidden="true" tabindex="-1"></a> <span class="fu">avaliar_modelo</span>(<span class="st">"PCA + SVM radial"</span>, cm_svm_pca),</span>
<span id="cb47-34"><a href="#cb47-34" aria-hidden="true" tabindex="-1"></a> <span class="fu">avaliar_modelo</span>(<span class="st">"Random Forest"</span>, cm_rf)</span>
<span id="cb47-35"><a href="#cb47-35" aria-hidden="true" tabindex="-1"></a>) <span class="sc">%&gt;%</span></span>
<span id="cb47-36"><a href="#cb47-36" aria-hidden="true" tabindex="-1"></a> <span class="fu">arrange</span>(<span class="fu">desc</span>(acuracia))</span>
<span id="cb47-37"><a href="#cb47-37" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb47-38"><a href="#cb47-38" aria-hidden="true" tabindex="-1"></a>resultados <span class="sc">%&gt;%</span></span>
<span id="cb47-39"><a href="#cb47-39" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="fu">across</span>(<span class="fu">where</span>(is.numeric), <span class="sc">~</span> <span class="fu">round</span>(.x, <span class="dv">4</span>))) <span class="sc">%&gt;%</span></span>
<span id="cb47-40"><a href="#cb47-40" aria-hidden="true" tabindex="-1"></a> <span class="fu">kable</span>(<span class="at">caption =</span> <span class="st">"Resultados - Dataset Completo (Holdout 70/30)"</span>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="cell-output-display">
<table class="caption-top table table-sm table-striped small">
<caption>Resultados - Dataset Completo (Holdout 70/30)</caption>
<colgroup>
<col style="width: 23%">
<col style="width: 10%">
<col style="width: 8%">
<col style="width: 23%">
<col style="width: 24%">
<col style="width: 10%">
</colgroup>
<thead>
<tr class="header">
<th style="text-align: left;">modelo</th>
<th style="text-align: right;">acuracia</th>
<th style="text-align: right;">kappa</th>
<th style="text-align: right;">sensibilidade_macro</th>
<th style="text-align: right;">especificidade_macro</th>
<th style="text-align: right;">f1_macro</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">SVM linear</td>
<td style="text-align: right;">0.3736</td>
<td style="text-align: right;">0.3214</td>
<td style="text-align: right;">0.3736</td>
<td style="text-align: right;">0.9478</td>
<td style="text-align: right;">0.3646</td>
</tr>
<tr class="even">
<td style="text-align: left;">SVM radial ajustado</td>
<td style="text-align: right;">0.3654</td>
<td style="text-align: right;">0.3125</td>
<td style="text-align: right;">0.3654</td>
<td style="text-align: right;">0.9471</td>
<td style="text-align: right;">0.3630</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Random Forest</td>
<td style="text-align: right;">0.3407</td>
<td style="text-align: right;">0.2857</td>
<td style="text-align: right;">0.3407</td>
<td style="text-align: right;">0.9451</td>
<td style="text-align: right;">0.3405</td>
</tr>
<tr class="even">
<td style="text-align: left;">PCA + SVM radial</td>
<td style="text-align: right;">0.3104</td>
<td style="text-align: right;">0.2530</td>
<td style="text-align: right;">0.3104</td>
<td style="text-align: right;">0.9425</td>
<td style="text-align: right;">0.3161</td>
</tr>
<tr class="odd">
<td style="text-align: left;">SVM radial</td>
<td style="text-align: right;">0.1484</td>
<td style="text-align: right;">0.0774</td>
<td style="text-align: right;">0.1484</td>
<td style="text-align: right;">0.9290</td>
<td style="text-align: right;">0.1673</td>
</tr>
</tbody>
</table>
</div>
</div>
<div class="cell">
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb48"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb48-1"><a href="#cb48-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(resultados, <span class="fu">aes</span>(<span class="at">x =</span> <span class="fu">reorder</span>(modelo, acuracia), <span class="at">y =</span> acuracia)) <span class="sc">+</span></span>
<span id="cb48-2"><a href="#cb48-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_col</span>(<span class="at">fill =</span> <span class="st">"#2C7FB8"</span>) <span class="sc">+</span></span>
<span id="cb48-3"><a href="#cb48-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">coord_flip</span>() <span class="sc">+</span></span>
<span id="cb48-4"><a href="#cb48-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
<span id="cb48-5"><a href="#cb48-5" aria-hidden="true" tabindex="-1"></a> <span class="at">title =</span> <span class="st">"Comparacao da acuracia dos modelos (dataset completo)"</span>,</span>
<span id="cb48-6"><a href="#cb48-6" aria-hidden="true" tabindex="-1"></a> <span class="at">x =</span> <span class="st">"Modelo"</span>,</span>
<span id="cb48-7"><a href="#cb48-7" aria-hidden="true" tabindex="-1"></a> <span class="at">y =</span> <span class="st">"Acuracia"</span></span>
<span id="cb48-8"><a href="#cb48-8" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">+</span></span>
<span id="cb48-9"><a href="#cb48-9" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme_minimal</span>()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="trabalho_svm_rgb_completo_files/figure-html/unnamed-chunk-20-1.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
</div>
</section>
<section id="importancia-das-variaveis" class="level1" data-number="16">
<h1 data-number="16"><span class="header-section-number">16</span> Importancia das variaveis</h1>
<div class="cell">
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb49"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb49-1"><a href="#cb49-1" aria-hidden="true" tabindex="-1"></a><span class="co"># ============================================================</span></span>
<span id="cb49-2"><a href="#cb49-2" aria-hidden="true" tabindex="-1"></a><span class="co"># 16. Importancia das variaveis</span></span>
<span id="cb49-3"><a href="#cb49-3" aria-hidden="true" tabindex="-1"></a><span class="co"># ============================================================</span></span>
<span id="cb49-4"><a href="#cb49-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb49-5"><a href="#cb49-5" aria-hidden="true" tabindex="-1"></a>importancia <span class="ot">&lt;-</span> <span class="fu">importance</span>(modelo_rf)</span>
<span id="cb49-6"><a href="#cb49-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb49-7"><a href="#cb49-7" aria-hidden="true" tabindex="-1"></a>coluna_importancia <span class="ot">&lt;-</span> <span class="cf">if</span> (<span class="st">"MeanDecreaseGini"</span> <span class="sc">%in%</span> <span class="fu">colnames</span>(importancia)) {</span>
<span id="cb49-8"><a href="#cb49-8" aria-hidden="true" tabindex="-1"></a> <span class="st">"MeanDecreaseGini"</span></span>
<span id="cb49-9"><a href="#cb49-9" aria-hidden="true" tabindex="-1"></a>} <span class="cf">else</span> {</span>
<span id="cb49-10"><a href="#cb49-10" aria-hidden="true" tabindex="-1"></a> <span class="fu">colnames</span>(importancia)[<span class="dv">1</span>]</span>
<span id="cb49-11"><a href="#cb49-11" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb49-12"><a href="#cb49-12" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb49-13"><a href="#cb49-13" aria-hidden="true" tabindex="-1"></a>importancia_df <span class="ot">&lt;-</span> <span class="fu">tibble</span>(</span>
<span id="cb49-14"><a href="#cb49-14" aria-hidden="true" tabindex="-1"></a> <span class="at">variavel =</span> <span class="fu">rownames</span>(importancia),</span>
<span id="cb49-15"><a href="#cb49-15" aria-hidden="true" tabindex="-1"></a> <span class="at">importancia =</span> importancia[, coluna_importancia]</span>
<span id="cb49-16"><a href="#cb49-16" aria-hidden="true" tabindex="-1"></a>) <span class="sc">%&gt;%</span></span>
<span id="cb49-17"><a href="#cb49-17" aria-hidden="true" tabindex="-1"></a> <span class="fu">arrange</span>(<span class="fu">desc</span>(importancia))</span>
<span id="cb49-18"><a href="#cb49-18" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb49-19"><a href="#cb49-19" aria-hidden="true" tabindex="-1"></a>importancia_df <span class="sc">%&gt;%</span></span>
<span id="cb49-20"><a href="#cb49-20" aria-hidden="true" tabindex="-1"></a> <span class="fu">slice_head</span>(<span class="at">n =</span> <span class="dv">20</span>) <span class="sc">%&gt;%</span></span>
<span id="cb49-21"><a href="#cb49-21" aria-hidden="true" tabindex="-1"></a> <span class="fu">kable</span>()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="cell-output-display">
<table class="caption-top table table-sm table-striped small">
<thead>
<tr class="header">
<th style="text-align: left;">variavel</th>
<th style="text-align: right;">importancia</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">feat_0472</td>
<td style="text-align: right;">2.569848</td>
</tr>
<tr class="even">
<td style="text-align: left;">feat_1003</td>
<td style="text-align: right;">1.884104</td>
</tr>
<tr class="odd">
<td style="text-align: left;">feat_0321</td>
<td style="text-align: right;">1.852070</td>
</tr>
<tr class="even">
<td style="text-align: left;">feat_0850</td>
<td style="text-align: right;">1.828746</td>
</tr>
<tr class="odd">
<td style="text-align: left;">feat_1091</td>
<td style="text-align: right;">1.736351</td>
</tr>
<tr class="even">
<td style="text-align: left;">feat_0476</td>
<td style="text-align: right;">1.654010</td>
</tr>
<tr class="odd">
<td style="text-align: left;">feat_0689</td>
<td style="text-align: right;">1.572648</td>
</tr>
<tr class="even">
<td style="text-align: left;">feat_0914</td>
<td style="text-align: right;">1.548601</td>
</tr>
<tr class="odd">
<td style="text-align: left;">feat_0562</td>
<td style="text-align: right;">1.521096</td>
</tr>
<tr class="even">
<td style="text-align: left;">feat_0260</td>
<td style="text-align: right;">1.492961</td>
</tr>
<tr class="odd">
<td style="text-align: left;">feat_0971</td>
<td style="text-align: right;">1.416875</td>
</tr>
<tr class="even">
<td style="text-align: left;">feat_1262</td>
<td style="text-align: right;">1.345804</td>
</tr>
<tr class="odd">
<td style="text-align: left;">feat_0778</td>
<td style="text-align: right;">1.340887</td>
</tr>
<tr class="even">
<td style="text-align: left;">feat_0473</td>
<td style="text-align: right;">1.325315</td>
</tr>
<tr class="odd">
<td style="text-align: left;">feat_0065</td>
<td style="text-align: right;">1.320592</td>
</tr>
<tr class="even">
<td style="text-align: left;">feat_0231</td>
<td style="text-align: right;">1.298018</td>
</tr>
<tr class="odd">
<td style="text-align: left;">feat_0780</td>
<td style="text-align: right;">1.296183</td>
</tr>
<tr class="even">
<td style="text-align: left;">feat_0278</td>
<td style="text-align: right;">1.287194</td>
</tr>
<tr class="odd">
<td style="text-align: left;">feat_0190</td>
<td style="text-align: right;">1.277953</td>
</tr>
<tr class="even">
<td style="text-align: left;">feat_0094</td>
<td style="text-align: right;">1.270837</td>
</tr>
</tbody>
</table>
</div>
</div>
</section>
<section id="salvamento-dos-resultados" class="level1" data-number="17">
<h1 data-number="17"><span class="header-section-number">17</span> Salvamento dos resultados</h1>
<div class="cell">
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb50"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb50-1"><a href="#cb50-1" aria-hidden="true" tabindex="-1"></a><span class="co"># ============================================================</span></span>
<span id="cb50-2"><a href="#cb50-2" aria-hidden="true" tabindex="-1"></a><span class="co"># 17. Salvar saidas (nomes diferentes do original)</span></span>
<span id="cb50-3"><a href="#cb50-3" aria-hidden="true" tabindex="-1"></a><span class="co"># ============================================================</span></span>
<span id="cb50-4"><a href="#cb50-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb50-5"><a href="#cb50-5" aria-hidden="true" tabindex="-1"></a><span class="fu">dir.create</span>(pasta_saida, <span class="at">recursive =</span> <span class="cn">TRUE</span>, <span class="at">showWarnings =</span> <span class="cn">FALSE</span>)</span>
<span id="cb50-6"><a href="#cb50-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb50-7"><a href="#cb50-7" aria-hidden="true" tabindex="-1"></a><span class="fu">write_csv</span>(dados, <span class="fu">file.path</span>(pasta_saida, <span class="st">"features_rgb_completo.csv"</span>))</span>
<span id="cb50-8"><a href="#cb50-8" aria-hidden="true" tabindex="-1"></a><span class="fu">write_csv</span>(resultados, <span class="fu">file.path</span>(pasta_saida, <span class="st">"resultados_modelos_rgb_completo.csv"</span>))</span>
<span id="cb50-9"><a href="#cb50-9" aria-hidden="true" tabindex="-1"></a><span class="fu">write_csv</span>(importancia_df, <span class="fu">file.path</span>(pasta_saida, <span class="st">"importancia_variaveis_rgb_completo.csv"</span>))</span>
<span id="cb50-10"><a href="#cb50-10" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb50-11"><a href="#cb50-11" aria-hidden="true" tabindex="-1"></a><span class="fu">saveRDS</span>(</span>
<span id="cb50-12"><a href="#cb50-12" aria-hidden="true" tabindex="-1"></a> <span class="fu">list</span>(</span>
<span id="cb50-13"><a href="#cb50-13" aria-hidden="true" tabindex="-1"></a> <span class="at">preproc =</span> preproc,</span>
<span id="cb50-14"><a href="#cb50-14" aria-hidden="true" tabindex="-1"></a> <span class="at">modelo_svm_linear =</span> modelo_svm_linear,</span>
<span id="cb50-15"><a href="#cb50-15" aria-hidden="true" tabindex="-1"></a> <span class="at">modelo_svm_radial =</span> modelo_svm_radial,</span>
<span id="cb50-16"><a href="#cb50-16" aria-hidden="true" tabindex="-1"></a> <span class="at">ajuste_svm_radial =</span> ajuste_svm_radial,</span>
<span id="cb50-17"><a href="#cb50-17" aria-hidden="true" tabindex="-1"></a> <span class="at">modelo_svm_radial_ajustado =</span> modelo_svm_radial_ajustado,</span>
<span id="cb50-18"><a href="#cb50-18" aria-hidden="true" tabindex="-1"></a> <span class="at">modelo_svm_pca =</span> modelo_svm_pca,</span>
<span id="cb50-19"><a href="#cb50-19" aria-hidden="true" tabindex="-1"></a> <span class="at">modelo_rf =</span> modelo_rf,</span>
<span id="cb50-20"><a href="#cb50-20" aria-hidden="true" tabindex="-1"></a> <span class="at">pca =</span> pca,</span>
<span id="cb50-21"><a href="#cb50-21" aria-hidden="true" tabindex="-1"></a> <span class="at">resultados =</span> resultados,</span>
<span id="cb50-22"><a href="#cb50-22" aria-hidden="true" tabindex="-1"></a> <span class="at">extrator_cnn =</span> <span class="st">"MobileNetV2 (ImageNet, sem topo, pooling=avg, 1280 features)"</span></span>
<span id="cb50-23"><a href="#cb50-23" aria-hidden="true" tabindex="-1"></a> ),</span>
<span id="cb50-24"><a href="#cb50-24" aria-hidden="true" tabindex="-1"></a> <span class="fu">file.path</span>(pasta_saida, <span class="st">"modelos_rgb_completo.rds"</span>)</span>
<span id="cb50-25"><a href="#cb50-25" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb50-26"><a href="#cb50-26" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb50-27"><a href="#cb50-27" aria-hidden="true" tabindex="-1"></a><span class="fu">cat</span>(<span class="st">"Arquivos salvos em:"</span>, pasta_saida, <span class="st">"</span><span class="sc">\n</span><span class="st">"</span>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="cell-output cell-output-stdout">
<pre><code>Arquivos salvos em: outputs/completo </code></pre>
</div>
</div>
</section>
<section id="validacao-cruzada-k-fold" class="level1" data-number="18">
<h1 data-number="18"><span class="header-section-number">18</span> Validacao Cruzada K-Fold</h1>
<div class="cell">
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb52"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb52-1"><a href="#cb52-1" aria-hidden="true" tabindex="-1"></a><span class="co"># ============================================================</span></span>
<span id="cb52-2"><a href="#cb52-2" aria-hidden="true" tabindex="-1"></a><span class="co"># 18. Validacao cruzada k-fold (k=5, estratificada)</span></span>
<span id="cb52-3"><a href="#cb52-3" aria-hidden="true" tabindex="-1"></a><span class="co"># ============================================================</span></span>
<span id="cb52-4"><a href="#cb52-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb52-5"><a href="#cb52-5" aria-hidden="true" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">123</span>)</span>
<span id="cb52-6"><a href="#cb52-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb52-7"><a href="#cb52-7" aria-hidden="true" tabindex="-1"></a>k_folds <span class="ot">&lt;-</span> <span class="dv">5</span>L</span>
<span id="cb52-8"><a href="#cb52-8" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb52-9"><a href="#cb52-9" aria-hidden="true" tabindex="-1"></a>folds <span class="ot">&lt;-</span> <span class="fu">createFolds</span>(dados<span class="sc">$</span>classe, <span class="at">k =</span> k_folds, <span class="at">list =</span> <span class="cn">TRUE</span>, <span class="at">returnTrain =</span> <span class="cn">FALSE</span>)</span>
<span id="cb52-10"><a href="#cb52-10" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb52-11"><a href="#cb52-11" aria-hidden="true" tabindex="-1"></a>grade_cost_kfold <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="dv">1</span>, <span class="dv">10</span>, <span class="dv">100</span>)</span>
<span id="cb52-12"><a href="#cb52-12" aria-hidden="true" tabindex="-1"></a>grade_gamma_kfold <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="fl">0.001</span>, <span class="fl">0.01</span>, <span class="fl">0.05</span>, <span class="fl">0.1</span>)</span>
<span id="cb52-13"><a href="#cb52-13" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb52-14"><a href="#cb52-14" aria-hidden="true" tabindex="-1"></a>metricas_folds <span class="ot">&lt;-</span> <span class="fu">vector</span>(<span class="st">"list"</span>, k_folds)</span>
<span id="cb52-15"><a href="#cb52-15" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb52-16"><a href="#cb52-16" aria-hidden="true" tabindex="-1"></a><span class="cf">for</span> (fold_i <span class="cf">in</span> <span class="fu">seq_len</span>(k_folds)) {</span>
<span id="cb52-17"><a href="#cb52-17" aria-hidden="true" tabindex="-1"></a> idx_teste <span class="ot">&lt;-</span> folds[[fold_i]]</span>
<span id="cb52-18"><a href="#cb52-18" aria-hidden="true" tabindex="-1"></a> idx_treino <span class="ot">&lt;-</span> <span class="fu">setdiff</span>(<span class="fu">seq_len</span>(<span class="fu">nrow</span>(dados)), idx_teste)</span>
<span id="cb52-19"><a href="#cb52-19" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb52-20"><a href="#cb52-20" aria-hidden="true" tabindex="-1"></a> fold_treino <span class="ot">&lt;-</span> dados[idx_treino, ]</span>
<span id="cb52-21"><a href="#cb52-21" aria-hidden="true" tabindex="-1"></a> fold_teste <span class="ot">&lt;-</span> dados[idx_teste, ]</span>
<span id="cb52-22"><a href="#cb52-22" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb52-23"><a href="#cb52-23" aria-hidden="true" tabindex="-1"></a> x_fold_tr <span class="ot">&lt;-</span> fold_treino <span class="sc">%&gt;%</span> <span class="fu">select</span>(<span class="sc">-</span><span class="fu">all_of</span>(colunas_nao_preditoras))</span>
<span id="cb52-24"><a href="#cb52-24" aria-hidden="true" tabindex="-1"></a> x_fold_te <span class="ot">&lt;-</span> fold_teste <span class="sc">%&gt;%</span> <span class="fu">select</span>(<span class="sc">-</span><span class="fu">all_of</span>(colunas_nao_preditoras))</span>
<span id="cb52-25"><a href="#cb52-25" aria-hidden="true" tabindex="-1"></a> y_fold_tr <span class="ot">&lt;-</span> <span class="fu">droplevels</span>(fold_treino<span class="sc">$</span>classe)</span>
<span id="cb52-26"><a href="#cb52-26" aria-hidden="true" tabindex="-1"></a> y_fold_te <span class="ot">&lt;-</span> <span class="fu">factor</span>(fold_teste<span class="sc">$</span>classe, <span class="at">levels =</span> <span class="fu">levels</span>(y_fold_tr))</span>
<span id="cb52-27"><a href="#cb52-27" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb52-28"><a href="#cb52-28" aria-hidden="true" tabindex="-1"></a> nzv_fold <span class="ot">&lt;-</span> <span class="fu">nearZeroVar</span>(x_fold_tr)</span>
<span id="cb52-29"><a href="#cb52-29" aria-hidden="true" tabindex="-1"></a> <span class="cf">if</span> (<span class="fu">length</span>(nzv_fold) <span class="sc">&gt;</span> <span class="dv">0</span>) {</span>
<span id="cb52-30"><a href="#cb52-30" aria-hidden="true" tabindex="-1"></a> x_fold_tr <span class="ot">&lt;-</span> x_fold_tr[, <span class="sc">-</span>nzv_fold, drop <span class="ot">=</span> <span class="cn">FALSE</span>]</span>
<span id="cb52-31"><a href="#cb52-31" aria-hidden="true" tabindex="-1"></a> x_fold_te <span class="ot">&lt;-</span> x_fold_te[, <span class="fu">colnames</span>(x_fold_tr), drop <span class="ot">=</span> <span class="cn">FALSE</span>]</span>
<span id="cb52-32"><a href="#cb52-32" aria-hidden="true" tabindex="-1"></a> }</span>
<span id="cb52-33"><a href="#cb52-33" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb52-34"><a href="#cb52-34" aria-hidden="true" tabindex="-1"></a> pp_fold <span class="ot">&lt;-</span> <span class="fu">preProcess</span>(x_fold_tr, <span class="at">method =</span> <span class="fu">c</span>(<span class="st">"center"</span>, <span class="st">"scale"</span>))</span>
<span id="cb52-35"><a href="#cb52-35" aria-hidden="true" tabindex="-1"></a> x_fold_tr_n <span class="ot">&lt;-</span> <span class="fu">predict</span>(pp_fold, x_fold_tr)</span>
<span id="cb52-36"><a href="#cb52-36" aria-hidden="true" tabindex="-1"></a> x_fold_te_n <span class="ot">&lt;-</span> <span class="fu">predict</span>(pp_fold, x_fold_te)</span>
<span id="cb52-37"><a href="#cb52-37" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb52-38"><a href="#cb52-38" aria-hidden="true" tabindex="-1"></a> m1 <span class="ot">&lt;-</span> <span class="fu">svm</span>(<span class="at">x =</span> x_fold_tr_n, <span class="at">y =</span> y_fold_tr,</span>
<span id="cb52-39"><a href="#cb52-39" aria-hidden="true" tabindex="-1"></a> <span class="at">kernel =</span> <span class="st">"linear"</span>, <span class="at">cost =</span> <span class="dv">1</span>, <span class="at">scale =</span> <span class="cn">FALSE</span>)</span>
<span id="cb52-40"><a href="#cb52-40" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb52-41"><a href="#cb52-41" aria-hidden="true" tabindex="-1"></a> m2 <span class="ot">&lt;-</span> <span class="fu">svm</span>(<span class="at">x =</span> x_fold_tr_n, <span class="at">y =</span> y_fold_tr,</span>
<span id="cb52-42"><a href="#cb52-42" aria-hidden="true" tabindex="-1"></a> <span class="at">kernel =</span> <span class="st">"radial"</span>, <span class="at">cost =</span> <span class="dv">10</span>, <span class="at">gamma =</span> <span class="fl">0.01</span>, <span class="at">scale =</span> <span class="cn">FALSE</span>)</span>
<span id="cb52-43"><a href="#cb52-43" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb52-44"><a href="#cb52-44" aria-hidden="true" tabindex="-1"></a> k_cv_fold <span class="ot">&lt;-</span> <span class="fu">min</span>(<span class="dv">5</span>L, <span class="fu">min</span>(<span class="fu">table</span>(y_fold_tr)))</span>
<span id="cb52-45"><a href="#cb52-45" aria-hidden="true" tabindex="-1"></a> ajuste_fold <span class="ot">&lt;-</span> <span class="fu">tune.svm</span>(</span>
<span id="cb52-46"><a href="#cb52-46" aria-hidden="true" tabindex="-1"></a> <span class="at">x =</span> x_fold_tr_n,</span>
<span id="cb52-47"><a href="#cb52-47" aria-hidden="true" tabindex="-1"></a> <span class="at">y =</span> y_fold_tr,</span>
<span id="cb52-48"><a href="#cb52-48" aria-hidden="true" tabindex="-1"></a> <span class="at">kernel =</span> <span class="st">"radial"</span>,</span>
<span id="cb52-49"><a href="#cb52-49" aria-hidden="true" tabindex="-1"></a> <span class="at">cost =</span> grade_cost_kfold,</span>
<span id="cb52-50"><a href="#cb52-50" aria-hidden="true" tabindex="-1"></a> <span class="at">gamma =</span> grade_gamma_kfold,</span>
<span id="cb52-51"><a href="#cb52-51" aria-hidden="true" tabindex="-1"></a> <span class="at">tunecontrol =</span> <span class="fu">tune.control</span>(<span class="at">cross =</span> k_cv_fold)</span>
<span id="cb52-52"><a href="#cb52-52" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb52-53"><a href="#cb52-53" aria-hidden="true" tabindex="-1"></a> m3 <span class="ot">&lt;-</span> ajuste_fold<span class="sc">$</span>best.model</span>
<span id="cb52-54"><a href="#cb52-54" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb52-55"><a href="#cb52-55" aria-hidden="true" tabindex="-1"></a> pca_fold <span class="ot">&lt;-</span> <span class="fu">prcomp</span>(x_fold_tr_n, <span class="at">center =</span> <span class="cn">FALSE</span>, <span class="at">scale. =</span> <span class="cn">FALSE</span>)</span>
<span id="cb52-56"><a href="#cb52-56" aria-hidden="true" tabindex="-1"></a> var_acum_f <span class="ot">&lt;-</span> <span class="fu">cumsum</span>(pca_fold<span class="sc">$</span>sdev<span class="sc">^</span><span class="dv">2</span> <span class="sc">/</span> <span class="fu">sum</span>(pca_fold<span class="sc">$</span>sdev<span class="sc">^</span><span class="dv">2</span>))</span>
<span id="cb52-57"><a href="#cb52-57" aria-hidden="true" tabindex="-1"></a> n_comp_f <span class="ot">&lt;-</span> <span class="fu">min</span>(<span class="fu">which</span>(var_acum_f <span class="sc">&gt;=</span> <span class="fl">0.95</span>)[<span class="dv">1</span>], <span class="dv">30</span>L, <span class="fu">ncol</span>(x_fold_tr_n))</span>
<span id="cb52-58"><a href="#cb52-58" aria-hidden="true" tabindex="-1"></a> x_pca_tr <span class="ot">&lt;-</span> <span class="fu">as.data.frame</span>(pca_fold<span class="sc">$</span>x[, <span class="dv">1</span><span class="sc">:</span>n_comp_f, <span class="at">drop =</span> <span class="cn">FALSE</span>])</span>
<span id="cb52-59"><a href="#cb52-59" aria-hidden="true" tabindex="-1"></a> x_pca_te <span class="ot">&lt;-</span> <span class="fu">as.data.frame</span>(<span class="fu">predict</span>(pca_fold, x_fold_te_n)[, <span class="dv">1</span><span class="sc">:</span>n_comp_f, <span class="at">drop =</span> <span class="cn">FALSE</span>])</span>
<span id="cb52-60"><a href="#cb52-60" aria-hidden="true" tabindex="-1"></a> m4 <span class="ot">&lt;-</span> <span class="fu">svm</span>(<span class="at">x =</span> x_pca_tr, <span class="at">y =</span> y_fold_tr,</span>
<span id="cb52-61"><a href="#cb52-61" aria-hidden="true" tabindex="-1"></a> <span class="at">kernel =</span> <span class="st">"radial"</span>, <span class="at">cost =</span> <span class="dv">10</span>, <span class="at">gamma =</span> <span class="fl">0.01</span>, <span class="at">scale =</span> <span class="cn">FALSE</span>)</span>
<span id="cb52-62"><a href="#cb52-62" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb52-63"><a href="#cb52-63" aria-hidden="true" tabindex="-1"></a> m5 <span class="ot">&lt;-</span> <span class="fu">randomForest</span>(<span class="at">x =</span> x_fold_tr_n, <span class="at">y =</span> y_fold_tr, <span class="at">ntree =</span> <span class="dv">200</span>L)</span>
<span id="cb52-64"><a href="#cb52-64" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb52-65"><a href="#cb52-65" aria-hidden="true" tabindex="-1"></a> metricas_folds[[fold_i]] <span class="ot">&lt;-</span> <span class="fu">bind_rows</span>(</span>
<span id="cb52-66"><a href="#cb52-66" aria-hidden="true" tabindex="-1"></a> <span class="fu">avaliar_modelo</span>(<span class="st">"SVM linear"</span>, <span class="fu">confusionMatrix</span>(<span class="fu">predict</span>(m1, x_fold_te_n), y_fold_te)),</span>
<span id="cb52-67"><a href="#cb52-67" aria-hidden="true" tabindex="-1"></a> <span class="fu">avaliar_modelo</span>(<span class="st">"SVM radial"</span>, <span class="fu">confusionMatrix</span>(<span class="fu">predict</span>(m2, x_fold_te_n), y_fold_te)),</span>
<span id="cb52-68"><a href="#cb52-68" aria-hidden="true" tabindex="-1"></a> <span class="fu">avaliar_modelo</span>(<span class="st">"SVM radial ajustado"</span>, <span class="fu">confusionMatrix</span>(<span class="fu">predict</span>(m3, x_fold_te_n), y_fold_te)),</span>
<span id="cb52-69"><a href="#cb52-69" aria-hidden="true" tabindex="-1"></a> <span class="fu">avaliar_modelo</span>(<span class="st">"PCA + SVM radial"</span>, <span class="fu">confusionMatrix</span>(<span class="fu">predict</span>(m4, x_pca_te), y_fold_te)),</span>
<span id="cb52-70"><a href="#cb52-70" aria-hidden="true" tabindex="-1"></a> <span class="fu">avaliar_modelo</span>(<span class="st">"Random Forest"</span>, <span class="fu">confusionMatrix</span>(<span class="fu">predict</span>(m5, x_fold_te_n), y_fold_te))</span>
<span id="cb52-71"><a href="#cb52-71" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">fold =</span> fold_i)</span>
<span id="cb52-72"><a href="#cb52-72" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb52-73"><a href="#cb52-73" aria-hidden="true" tabindex="-1"></a> <span class="fu">cat</span>(<span class="fu">sprintf</span>(</span>
<span id="cb52-74"><a href="#cb52-74" aria-hidden="true" tabindex="-1"></a> <span class="st">"Fold %d/%d concluido. SVM ajustado: cost = %s | gamma = %s</span><span class="sc">\n</span><span class="st">"</span>,</span>
<span id="cb52-75"><a href="#cb52-75" aria-hidden="true" tabindex="-1"></a> fold_i, k_folds,</span>
<span id="cb52-76"><a href="#cb52-76" aria-hidden="true" tabindex="-1"></a> ajuste_fold<span class="sc">$</span>best.parameters<span class="sc">$</span>cost,</span>
<span id="cb52-77"><a href="#cb52-77" aria-hidden="true" tabindex="-1"></a> ajuste_fold<span class="sc">$</span>best.parameters<span class="sc">$</span>gamma</span>
<span id="cb52-78"><a href="#cb52-78" aria-hidden="true" tabindex="-1"></a> ))</span>
<span id="cb52-79"><a href="#cb52-79" aria-hidden="true" tabindex="-1"></a>}</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="cell-output cell-output-stdout">
<pre><code>Fold 1/5 concluido. SVM ajustado: cost = 10 | gamma = 0.001
Fold 2/5 concluido. SVM ajustado: cost = 10 | gamma = 0.001
Fold 3/5 concluido. SVM ajustado: cost = 10 | gamma = 0.001
Fold 4/5 concluido. SVM ajustado: cost = 10 | gamma = 0.001
Fold 5/5 concluido. SVM ajustado: cost = 10 | gamma = 0.001</code></pre>
</div>
</div>
<div class="cell">
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb54"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb54-1"><a href="#cb54-1" aria-hidden="true" tabindex="-1"></a>resultados_kfold <span class="ot">&lt;-</span> <span class="fu">bind_rows</span>(metricas_folds) <span class="sc">%&gt;%</span></span>
<span id="cb54-2"><a href="#cb54-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">group_by</span>(modelo) <span class="sc">%&gt;%</span></span>
<span id="cb54-3"><a href="#cb54-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">summarise</span>(<span class="fu">across</span>(<span class="fu">c</span>(acuracia, kappa, sensibilidade_macro, especificidade_macro, f1_macro), mean),</span>
<span id="cb54-4"><a href="#cb54-4" aria-hidden="true" tabindex="-1"></a> <span class="at">.groups =</span> <span class="st">"drop"</span>) <span class="sc">%&gt;%</span></span>
<span id="cb54-5"><a href="#cb54-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">arrange</span>(<span class="fu">desc</span>(acuracia))</span>
<span id="cb54-6"><a href="#cb54-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb54-7"><a href="#cb54-7" aria-hidden="true" tabindex="-1"></a><span class="fu">write_csv</span>(resultados_kfold, <span class="fu">file.path</span>(pasta_saida, <span class="st">"resultados_kfold_rgb_completo.csv"</span>))</span>
<span id="cb54-8"><a href="#cb54-8" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb54-9"><a href="#cb54-9" aria-hidden="true" tabindex="-1"></a>resultados_kfold <span class="sc">%&gt;%</span></span>
<span id="cb54-10"><a href="#cb54-10" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="fu">across</span>(<span class="fu">where</span>(is.numeric), <span class="sc">~</span> <span class="fu">round</span>(.x, <span class="dv">4</span>))) <span class="sc">%&gt;%</span></span>
<span id="cb54-11"><a href="#cb54-11" aria-hidden="true" tabindex="-1"></a> <span class="fu">kable</span>(<span class="at">caption =</span> <span class="st">"Acuracia media - K-Fold (k=5) - Dataset Completo"</span>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="cell-output-display">
<table class="caption-top table table-sm table-striped small">
<caption>Acuracia media - K-Fold (k=5) - Dataset Completo</caption>
<colgroup>
<col style="width: 23%">
<col style="width: 10%">
<col style="width: 8%">
<col style="width: 23%">
<col style="width: 24%">
<col style="width: 10%">
</colgroup>
<thead>
<tr class="header">
<th style="text-align: left;">modelo</th>
<th style="text-align: right;">acuracia</th>
<th style="text-align: right;">kappa</th>
<th style="text-align: right;">sensibilidade_macro</th>
<th style="text-align: right;">especificidade_macro</th>
<th style="text-align: right;">f1_macro</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">SVM linear</td>
<td style="text-align: right;">0.3630</td>
<td style="text-align: right;">0.3100</td>
<td style="text-align: right;">0.3634</td>
<td style="text-align: right;">0.9469</td>
<td style="text-align: right;">0.3835</td>
</tr>
<tr class="even">
<td style="text-align: left;">SVM radial ajustado</td>
<td style="text-align: right;">0.3565</td>
<td style="text-align: right;">0.3029</td>
<td style="text-align: right;">0.3568</td>
<td style="text-align: right;">0.9464</td>
<td style="text-align: right;">0.3721</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Random Forest</td>
<td style="text-align: right;">0.3285</td>
<td style="text-align: right;">0.2726</td>
<td style="text-align: right;">0.3288</td>
<td style="text-align: right;">0.9440</td>
<td style="text-align: right;">0.3406</td>
</tr>
<tr class="even">
<td style="text-align: left;">PCA + SVM radial</td>
<td style="text-align: right;">0.3236</td>
<td style="text-align: right;">0.2674</td>
<td style="text-align: right;">0.3239</td>
<td style="text-align: right;">0.9436</td>
<td style="text-align: right;">0.3593</td>
</tr>
<tr class="odd">
<td style="text-align: left;">SVM radial</td>
<td style="text-align: right;">0.1419</td>
<td style="text-align: right;">0.0709</td>
<td style="text-align: right;">0.1433</td>
<td style="text-align: right;">0.9285</td>
<td style="text-align: right;">0.1743</td>
</tr>
</tbody>
</table>
</div>
</div>
</section>
<section id="comparacao-holdout-vs-k-fold" class="level1" data-number="19">
<h1 data-number="19"><span class="header-section-number">19</span> Comparacao: Holdout vs K-Fold</h1>
<div class="cell">
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb55"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb55-1"><a href="#cb55-1" aria-hidden="true" tabindex="-1"></a><span class="co"># ============================================================</span></span>
<span id="cb55-2"><a href="#cb55-2" aria-hidden="true" tabindex="-1"></a><span class="co"># 19. Comparacao direta entre as duas estrategias</span></span>
<span id="cb55-3"><a href="#cb55-3" aria-hidden="true" tabindex="-1"></a><span class="co"># ============================================================</span></span>
<span id="cb55-4"><a href="#cb55-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb55-5"><a href="#cb55-5" aria-hidden="true" tabindex="-1"></a>comparacao <span class="ot">&lt;-</span> <span class="fu">bind_rows</span>(</span>
<span id="cb55-6"><a href="#cb55-6" aria-hidden="true" tabindex="-1"></a> resultados <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">estrategia =</span> <span class="st">"Holdout 70/30"</span>),</span>
<span id="cb55-7"><a href="#cb55-7" aria-hidden="true" tabindex="-1"></a> resultados_kfold <span class="sc">%&gt;%</span> <span class="fu">mutate</span>(<span class="at">estrategia =</span> <span class="st">"K-Fold (k=5)"</span>)</span>
<span id="cb55-8"><a href="#cb55-8" aria-hidden="true" tabindex="-1"></a>) <span class="sc">%&gt;%</span></span>
<span id="cb55-9"><a href="#cb55-9" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(estrategia, modelo, acuracia, kappa, f1_macro) <span class="sc">%&gt;%</span></span>
<span id="cb55-10"><a href="#cb55-10" aria-hidden="true" tabindex="-1"></a> <span class="fu">arrange</span>(estrategia, <span class="fu">desc</span>(acuracia))</span>
<span id="cb55-11"><a href="#cb55-11" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb55-12"><a href="#cb55-12" aria-hidden="true" tabindex="-1"></a>comparacao <span class="sc">%&gt;%</span></span>
<span id="cb55-13"><a href="#cb55-13" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="fu">across</span>(<span class="fu">where</span>(is.numeric), <span class="sc">~</span> <span class="fu">round</span>(.x, <span class="dv">4</span>))) <span class="sc">%&gt;%</span></span>
<span id="cb55-14"><a href="#cb55-14" aria-hidden="true" tabindex="-1"></a> <span class="fu">kable</span>(<span class="at">caption =</span> <span class="st">"Comparacao entre Holdout 70/30 e K-Fold - Dataset Completo"</span>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="cell-output-display">
<table class="caption-top table table-sm table-striped small">
<caption>Comparacao entre Holdout 70/30 e K-Fold - Dataset Completo</caption>
<thead>
<tr class="header">
<th style="text-align: left;">estrategia</th>
<th style="text-align: left;">modelo</th>
<th style="text-align: right;">acuracia</th>
<th style="text-align: right;">kappa</th>
<th style="text-align: right;">f1_macro</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;">Holdout 70/30</td>
<td style="text-align: left;">SVM linear</td>
<td style="text-align: right;">0.3736</td>
<td style="text-align: right;">0.3214</td>
<td style="text-align: right;">0.3646</td>
</tr>
<tr class="even">
<td style="text-align: left;">Holdout 70/30</td>
<td style="text-align: left;">SVM radial ajustado</td>
<td style="text-align: right;">0.3654</td>
<td style="text-align: right;">0.3125</td>
<td style="text-align: right;">0.3630</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Holdout 70/30</td>
<td style="text-align: left;">Random Forest</td>
<td style="text-align: right;">0.3407</td>
<td style="text-align: right;">0.2857</td>
<td style="text-align: right;">0.3405</td>
</tr>
<tr class="even">
<td style="text-align: left;">Holdout 70/30</td>
<td style="text-align: left;">PCA + SVM radial</td>
<td style="text-align: right;">0.3104</td>
<td style="text-align: right;">0.2530</td>
<td style="text-align: right;">0.3161</td>
</tr>
<tr class="odd">
<td style="text-align: left;">Holdout 70/30</td>
<td style="text-align: left;">SVM radial</td>
<td style="text-align: right;">0.1484</td>
<td style="text-align: right;">0.0774</td>
<td style="text-align: right;">0.1673</td>
</tr>
<tr class="even">
<td style="text-align: left;">K-Fold (k=5)</td>
<td style="text-align: left;">SVM linear</td>
<td style="text-align: right;">0.3630</td>
<td style="text-align: right;">0.3100</td>
<td style="text-align: right;">0.3835</td>
</tr>
<tr class="odd">
<td style="text-align: left;">K-Fold (k=5)</td>
<td style="text-align: left;">SVM radial ajustado</td>
<td style="text-align: right;">0.3565</td>
<td style="text-align: right;">0.3029</td>
<td style="text-align: right;">0.3721</td>
</tr>
<tr class="even">
<td style="text-align: left;">K-Fold (k=5)</td>
<td style="text-align: left;">Random Forest</td>
<td style="text-align: right;">0.3285</td>
<td style="text-align: right;">0.2726</td>
<td style="text-align: right;">0.3406</td>
</tr>
<tr class="odd">
<td style="text-align: left;">K-Fold (k=5)</td>
<td style="text-align: left;">PCA + SVM radial</td>
<td style="text-align: right;">0.3236</td>
<td style="text-align: right;">0.2674</td>
<td style="text-align: right;">0.3593</td>
</tr>
<tr class="even">
<td style="text-align: left;">K-Fold (k=5)</td>
<td style="text-align: left;">SVM radial</td>
<td style="text-align: right;">0.1419</td>
<td style="text-align: right;">0.0709</td>
<td style="text-align: right;">0.1743</td>
</tr>
</tbody>
</table>
</div>
</div>
<div class="cell">
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb56"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb56-1"><a href="#cb56-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(comparacao, <span class="fu">aes</span>(<span class="at">x =</span> <span class="fu">reorder</span>(modelo, acuracia), <span class="at">y =</span> acuracia, <span class="at">fill =</span> estrategia)) <span class="sc">+</span></span>
<span id="cb56-2"><a href="#cb56-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_col</span>(<span class="at">position =</span> <span class="st">"dodge"</span>) <span class="sc">+</span></span>
<span id="cb56-3"><a href="#cb56-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">coord_flip</span>() <span class="sc">+</span></span>
<span id="cb56-4"><a href="#cb56-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">scale_fill_manual</span>(<span class="at">values =</span> <span class="fu">c</span>(<span class="st">"Holdout 70/30"</span> <span class="ot">=</span> <span class="st">"#2C7FB8"</span>, <span class="st">"K-Fold (k=5)"</span> <span class="ot">=</span> <span class="st">"#E06C00"</span>)) <span class="sc">+</span></span>
<span id="cb56-5"><a href="#cb56-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(</span>
<span id="cb56-6"><a href="#cb56-6" aria-hidden="true" tabindex="-1"></a> <span class="at">title =</span> <span class="st">"Acuracia: Holdout 70/30 vs K-Fold (k=5) - Dataset Completo"</span>,</span>
<span id="cb56-7"><a href="#cb56-7" aria-hidden="true" tabindex="-1"></a> <span class="at">x =</span> <span class="st">"Modelo"</span>,</span>
<span id="cb56-8"><a href="#cb56-8" aria-hidden="true" tabindex="-1"></a> <span class="at">y =</span> <span class="st">"Acuracia"</span>,</span>
<span id="cb56-9"><a href="#cb56-9" aria-hidden="true" tabindex="-1"></a> <span class="at">fill =</span> <span class="st">"Estrategia"</span></span>
<span id="cb56-10"><a href="#cb56-10" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">+</span></span>
<span id="cb56-11"><a href="#cb56-11" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme_minimal</span>()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="cell-output-display">
<div>
<figure class="figure">
<p><img src="trabalho_svm_rgb_completo_files/figure-html/unnamed-chunk-26-1.png" class="img-fluid figure-img" width="672"></p>
</figure>
</div>
</div>
</div>
</section>
<section id="conclusao" class="level1" data-number="20">
<h1 data-number="20"><span class="header-section-number">20</span> Conclusao</h1>
<div class="cell">
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb57"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb57-1"><a href="#cb57-1" aria-hidden="true" tabindex="-1"></a>melhor_holdout <span class="ot">&lt;-</span> <span class="fu">max</span>(resultados<span class="sc">$</span>acuracia)</span>
<span id="cb57-2"><a href="#cb57-2" aria-hidden="true" tabindex="-1"></a>melhor_kf <span class="ot">&lt;-</span> <span class="fu">max</span>(resultados_kfold<span class="sc">$</span>acuracia)</span>
<span id="cb57-3"><a href="#cb57-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb57-4"><a href="#cb57-4" aria-hidden="true" tabindex="-1"></a><span class="fu">cat</span>(<span class="st">"Acuracia maxima - Holdout:"</span>, <span class="fu">round</span>(melhor_holdout, <span class="dv">4</span>), <span class="st">"</span><span class="sc">\n</span><span class="st">"</span>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="cell-output cell-output-stdout">
<pre><code>Acuracia maxima - Holdout: 0.3736 </code></pre>
</div>
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb59"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb59-1"><a href="#cb59-1" aria-hidden="true" tabindex="-1"></a><span class="fu">cat</span>(<span class="st">"Acuracia maxima - K-Fold: "</span>, <span class="fu">round</span>(melhor_kf, <span class="dv">4</span>), <span class="st">"</span><span class="sc">\n</span><span class="st">"</span>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="cell-output cell-output-stdout">
<pre><code>Acuracia maxima - K-Fold: 0.363 </code></pre>
</div>
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb61"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb61-1"><a href="#cb61-1" aria-hidden="true" tabindex="-1"></a><span class="fu">cat</span>(<span class="st">"Melhor modelo (holdout):"</span>, resultados<span class="sc">$</span>modelo[<span class="dv">1</span>], <span class="st">"</span><span class="sc">\n</span><span class="st">"</span>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="cell-output cell-output-stdout">
<pre><code>Melhor modelo (holdout): SVM linear </code></pre>
</div>
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb63"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb63-1"><a href="#cb63-1" aria-hidden="true" tabindex="-1"></a><span class="fu">cat</span>(<span class="st">"Melhor modelo (k-fold):"</span>, resultados_kfold<span class="sc">$</span>modelo[<span class="dv">1</span>], <span class="st">"</span><span class="sc">\n</span><span class="st">"</span>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="cell-output cell-output-stdout">
<pre><code>Melhor modelo (k-fold): SVM linear </code></pre>
</div>
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