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<!DOCTYPE html>
<html lang="en"><head>
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<title>7. Data Modeling</title>
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</head>
<body class="quarto-light">
<div class="reveal">
<div class="slides">
<section id="title-slide" class="quarto-title-block center">
<h1 class="title">7. Data Modeling</h1>
<div class="quarto-title-authors">
<div class="quarto-title-author">
<div class="quarto-title-author-name">
Yi-Ju Tseng
</div>
</div>
</div>
</section>
<section id="資料分析步驟" class="slide level2">
<h2>資料分析步驟</h2>
<ul>
<li>資料匯入</li>
<li>資料清洗處理</li>
<li>資料分析</li>
<li>資料呈現與視覺化</li>
<li><strong>建模</strong></li>
</ul>
</section>
<section id="資料建模" class="slide level2">
<h2>資料建模</h2>
<ul>
<li>機器學習簡介</li>
<li>AutoML</li>
<li>深度學習簡介</li>
<li>AutoKeras</li>
<li>(補充資料)scikit-learn - ML with Python 常用套件</li>
<li>(補充資料)keras - DL with Python 常用套件</li>
</ul>
</section>
<section id="前置作業" class="slide level2">
<h2>前置作業</h2>
<p>為了成功從https (加密封包傳輸)下載資料，首先取消證書驗證</p>
<div class="cell" data-execution_count="1">
<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode numberSource python number-lines code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1"></a><span class="im">import</span> ssl</span>
<span id="cb1-2"><a href="#cb1-2"></a>ssl._create_default_https_context <span class="op">=</span> ssl._create_unverified_context</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</section>
<section id="什麼案子可以用ai" class="slide level2">
<h2>什麼案子可以用AI?</h2>
<ul>
<li>有資料</li>
<li>學的會</li>
<li>學的好有很大的貢獻</li>
<li>對AI接受度高</li>
</ul>
<p>最後….</p>
<p>做個Proof of concept看看會不會成 (快速好用的軟體)</p>
</section>
<section id="資料建模-1" class="slide level2">
<h2>資料建模</h2>
<ul>
<li><strong>機器學習簡介</strong></li>
<li>AutoML</li>
<li>深度學習簡介</li>
<li>AutoKeras</li>
<li>(補充資料)scikit-learn - ML with Python 常用套件</li>
<li>(補充資料)keras - DL with Python 常用套件</li>
</ul>
</section>
<section>
<section id="machine-learning-機器學習簡介" class="title-slide slide level1 center">
<h1>Machine Learning 機器學習簡介</h1>
</section>
<section id="機器學習簡介" class="slide level2">
<h2>機器學習簡介</h2>
<p>從輸入資料學習新資訊，用來預測事件或協助決策</p>
<ul>
<li>Classical Learning 傳統的機器學習
<ul>
<li>Supervised learning 監督式學習</li>
<li>Unsupervised learning 非監督式學習</li>
</ul></li>
<li>Ensemble Method 集成方法
<ul>
<li>Stacking</li>
<li>Bagging</li>
<li>Boosting</li>
</ul></li>
<li>Reinforcement Learning 強化學習</li>
</ul>
</section>
<section id="classical-learning-傳統的機器學習" class="slide level2">
<h2>Classical Learning 傳統的機器學習</h2>
<img data-src="https://i.vas3k.blog/7w1.jpg" class="r-stretch"><p><a href="https://vas3k.com/blog/machine_learning/">Source</a></p>
</section>
<section id="監督式學習-supervised-learning" class="slide level2">
<h2>監督式學習 Supervised learning</h2>
<p>有答案的資料</p>
<ul>
<li>Regression 迴歸：真實的’值’（股票、氣溫）
<ul>
<li>Linear Regression 線性迴歸</li>
<li>Support Vector Regression (SVR)</li>
<li>Decision Tree Regression</li>
</ul></li>
</ul>
</section>
<section id="監督式學習-supervised-learning-1" class="slide level2">
<h2>監督式學習 Supervised learning</h2>
<p>有答案的資料</p>
<ul>
<li>Classification 分類：分兩類（P/N, Yes/No, M/F, Sick/Not sick）/分多類 (A/B/C/D)
<ul>
<li>Logistic Regression 羅吉斯迴歸、邏輯迴歸</li>
<li>Support Vector Machines 支持向量機</li>
<li>Decision Trees 決策樹</li>
<li>K-Nearest Neighbor</li>
<li>Artificial Neural Networks 類神經網路</li>
<li>Deep Learning 深度學習</li>
</ul></li>
</ul>
</section>
<section id="非監督式學習-unsupervised-learning" class="slide level2">
<h2>非監督式學習 Unsupervised learning</h2>
<p>沒有答案的資料</p>
<ul>
<li>Clustering 分群
<ul>
<li>Hierarchical clustering 階層式分群</li>
<li>K-means clustering</li>
<li>Artificial Neural Networks 類神經網路</li>
<li>Deep Learning 深度學習</li>
</ul></li>
<li>Association Rules 關聯式規則</li>
</ul>
</section>
<section id="ensemble-method-集成方法" class="slide level2">
<h2>Ensemble Method 集成方法</h2>
<ul>
<li>Bagging
<ul>
<li>Bootstrap aggregating，套袋法</li>
<li>Random Forest</li>
</ul></li>
<li>Boosting
<ul>
<li>XGBoost</li>
<li>LightGBM</li>
</ul></li>
</ul>
</section>
<section id="模型驗證" class="slide level2">
<h2>模型驗證</h2>
<ul>
<li>在完成模型訓練後，為了驗證模型訓練的好不好，需要用一組<strong>獨立的測試資料</strong>，來做模型的驗證</li>
<li>在訓練模型前，必須特別留意是否有保留一份<strong>獨立的測試資料</strong>，並確保在訓練模型時都不用到此獨立資料集</li>
<li>資料集可分為以下兩種：
<ul>
<li><strong>訓練組Training set</strong>, Development set: 讓演算法學到知識</li>
<li><strong>測試組Test set</strong>, Validation set: 驗證學的怎麼樣</li>
</ul></li>
</ul>
</section>
<section id="模型驗證方法" class="slide level2">
<h2>模型驗證方法</h2>
<img data-src="images/clipboard-1611147261.png" class="r-stretch"></section>
<section id="資料建模-2" class="slide level2">
<h2>資料建模</h2>
<ul>
<li>機器學習簡介</li>
<li><strong>AutoML</strong></li>
<li>深度學習簡介</li>
<li>AutoKeras</li>
<li>(補充資料)scikit-learn - ML with Python 常用套件</li>
<li>(補充資料)keras - DL with Python 常用套件</li>
</ul>
</section></section>
<section>
<section id="automl" class="title-slide slide level1 center">
<h1>AutoML</h1>
</section>
<section id="automl-1" class="slide level2">
<h2>AutoML</h2>
<ul>
<li>AutoML為快速建模的工具</li>
<li>市面上有許多AutoML的套件，包括 <code>pycaret</code></li>
<li><code>scikit-learn</code>則是在python中執行機器學習模型訓練的重要套件</li>
</ul>
<div class="cell" data-execution_count="2">
<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode numberSource python number-lines code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1"></a><span class="op">!</span>pip install pycaret scikit<span class="op">-</span>learn</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</section>
<section id="載入所需套件" class="slide level2">
<h2>載入所需套件</h2>
<div class="cell" data-execution_count="3">
<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode numberSource python number-lines code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1"></a><span class="im">import</span> numpy <span class="im">as</span> np</span>
<span id="cb3-2"><a href="#cb3-2"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
<span id="cb3-3"><a href="#cb3-3"></a><span class="im">import</span> seaborn <span class="im">as</span> sb <span class="co">#畫圖</span></span>
<span id="cb3-4"><a href="#cb3-4"></a><span class="im">import</span> scipy <span class="co">#統計</span></span>
<span id="cb3-5"><a href="#cb3-5"></a></span>
<span id="cb3-6"><a href="#cb3-6"></a><span class="co"># 載入PyCaret AutoML套件</span></span>
<span id="cb3-7"><a href="#cb3-7"></a><span class="im">import</span> pycaret</span>
<span id="cb3-8"><a href="#cb3-8"></a><span class="im">from</span> pycaret.classification <span class="im">import</span> <span class="op">*</span></span>
<span id="cb3-9"><a href="#cb3-9"></a><span class="im">from</span> sklearn.model_selection <span class="im">import</span> train_test_split</span>
<span id="cb3-10"><a href="#cb3-10"></a><span class="im">from</span> sklearn <span class="im">import</span> datasets</span>
<span id="cb3-11"><a href="#cb3-11"></a><span class="im">from</span> sklearn <span class="im">import</span> metrics</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</section>
<section id="載入資料" class="slide level2">
<h2>載入資料</h2>
<div class="cell" data-execution_count="4">
<div class="sourceCode cell-code" id="cb4"><pre class="sourceCode numberSource python number-lines code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1"></a>cancer <span class="op">=</span> datasets.load_breast_cancer()</span>
<span id="cb4-2"><a href="#cb4-2"></a>X <span class="op">=</span> pd.DataFrame(cancer[<span class="st">"data"</span>], columns<span class="op">=</span>cancer[<span class="st">"feature_names"</span>])</span>
<span id="cb4-3"><a href="#cb4-3"></a>y <span class="op">=</span> pd.DataFrame(cancer[<span class="st">"target"</span>], columns<span class="op">=</span>[<span class="st">"target"</span>])</span>
<span id="cb4-4"><a href="#cb4-4"></a><span class="bu">print</span>(X.head())</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code> mean radius mean texture mean perimeter mean area mean smoothness \
0 17.99 10.38 122.80 1001.0 0.11840
1 20.57 17.77 132.90 1326.0 0.08474
2 19.69 21.25 130.00 1203.0 0.10960
3 11.42 20.38 77.58 386.1 0.14250
4 20.29 14.34 135.10 1297.0 0.10030
mean compactness mean concavity mean concave points mean symmetry \
0 0.27760 0.3001 0.14710 0.2419
1 0.07864 0.0869 0.07017 0.1812
2 0.15990 0.1974 0.12790 0.2069
3 0.28390 0.2414 0.10520 0.2597
4 0.13280 0.1980 0.10430 0.1809
mean fractal dimension ... worst radius worst texture worst perimeter \
0 0.07871 ... 25.38 17.33 184.60
1 0.05667 ... 24.99 23.41 158.80
2 0.05999 ... 23.57 25.53 152.50
3 0.09744 ... 14.91 26.50 98.87
4 0.05883 ... 22.54 16.67 152.20
worst area worst smoothness worst compactness worst concavity \
0 2019.0 0.1622 0.6656 0.7119
1 1956.0 0.1238 0.1866 0.2416
2 1709.0 0.1444 0.4245 0.4504
3 567.7 0.2098 0.8663 0.6869
4 1575.0 0.1374 0.2050 0.4000
worst concave points worst symmetry worst fractal dimension
0 0.2654 0.4601 0.11890
1 0.1860 0.2750 0.08902
2 0.2430 0.3613 0.08758
3 0.2575 0.6638 0.17300
4 0.1625 0.2364 0.07678
[5 rows x 30 columns]</code></pre>
</div>
</div>
</section>
<section id="拆分成訓練集與測試集" class="slide level2">
<h2>拆分成訓練集與測試集</h2>
<ul>
<li><code>train_test_split(X,y,test_size=比例,random_state=隨機種子)</code></li>
<li>依照設定比例將資料隨機分為訓練組與測試組</li>
</ul>
<div class="cell" data-execution_count="5">
<div class="sourceCode cell-code" id="cb6"><pre class="sourceCode numberSource python number-lines code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1"></a>X_train, X_test, y_train, y_test <span class="op">=</span> train_test_split(X, y, test_size<span class="op">=</span><span class="fl">0.3</span>, random_state<span class="op">=</span><span class="dv">42</span>)</span>
<span id="cb6-2"><a href="#cb6-2"></a><span class="bu">print</span>(X_train.size)</span>
<span id="cb6-3"><a href="#cb6-3"></a><span class="bu">print</span>(X_test.size)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>11940
5130</code></pre>
</div>
</div>
</section>
<section id="整合訓練集" class="slide level2">
<h2>整合訓練集</h2>
<p><code>pd.concat([df1,df2], axis = 1)</code> 左右整合<code>df1</code>和<code>df2</code></p>
<div class="cell" data-execution_count="6">
<div class="sourceCode cell-code" id="cb8"><pre class="sourceCode numberSource python number-lines code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1"></a>train <span class="op">=</span> pd.concat([X_train,y_train], axis<span class="op">=</span><span class="dv">1</span>)</span>
<span id="cb8-2"><a href="#cb8-2"></a><span class="bu">print</span>(train.head())</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code> mean radius mean texture mean perimeter mean area mean smoothness \
149 13.74 17.91 88.12 585.0 0.07944
124 13.37 16.39 86.10 553.5 0.07115
421 14.69 13.98 98.22 656.1 0.10310
195 12.91 16.33 82.53 516.4 0.07941
545 13.62 23.23 87.19 573.2 0.09246
mean compactness mean concavity mean concave points mean symmetry \
149 0.06376 0.02881 0.01329 0.1473
124 0.07325 0.08092 0.02800 0.1422
421 0.18360 0.14500 0.06300 0.2086
195 0.05366 0.03873 0.02377 0.1829
545 0.06747 0.02974 0.02443 0.1664
mean fractal dimension ... worst texture worst perimeter worst area \
149 0.05580 ... 22.46 97.19 725.9
124 0.05823 ... 22.75 91.99 632.1
421 0.07406 ... 18.34 114.10 809.2
195 0.05667 ... 22.00 90.81 600.6
545 0.05801 ... 29.09 97.58 729.8
worst smoothness worst compactness worst concavity \
149 0.09711 0.1824 0.1564
124 0.10250 0.2531 0.3308
421 0.13120 0.3635 0.3219
195 0.10970 0.1506 0.1764
545 0.12160 0.1517 0.1049
worst concave points worst symmetry worst fractal dimension target
149 0.06019 0.2350 0.07014 1
124 0.08978 0.2048 0.07628 1
421 0.11080 0.2827 0.09208 1
195 0.08235 0.3024 0.06949 1
545 0.07174 0.2642 0.06953 1
[5 rows x 31 columns]</code></pre>
</div>
</div>
</section>
<section id="自動訓練" class="slide level2">
<h2>自動訓練</h2>
<ul>
<li><code>setup(有標籤的訓練資料,targe=標籤名稱)</code> 設定 PyCaret AutoML環境</li>
<li><code>compare_models()</code> 選擇最好的模型</li>
</ul>
<div class="cell" data-execution_count="7">
<div class="sourceCode cell-code" id="cb10"><pre class="sourceCode numberSource python number-lines code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1"></a>automlclassifier <span class="op">=</span> setup(train, target<span class="op">=</span><span class="st">"target"</span>)</span>
<span id="cb10-2"><a href="#cb10-2"></a>best_model <span class="op">=</span> compare_models()</span>
<span id="cb10-3"><a href="#cb10-3"></a><span class="bu">print</span>(best_model)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-display">
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#T_bb952_row8_col1 {
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</style>
<table id="T_bb952" data-quarto-postprocess="true">
<thead>
<tr class="header">
<th class="blank level0" data-quarto-table-cell-role="th"> </th>
<th id="T_bb952_level0_col0" class="col_heading level0 col0" data-quarto-table-cell-role="th">Description</th>
<th id="T_bb952_level0_col1" class="col_heading level0 col1" data-quarto-table-cell-role="th">Value</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td id="T_bb952_level0_row0" class="row_heading level0 row0" data-quarto-table-cell-role="th">0</td>
<td id="T_bb952_row0_col0" class="data row0 col0">Session id</td>
<td id="T_bb952_row0_col1" class="data row0 col1">5552</td>
</tr>
<tr class="even">
<td id="T_bb952_level0_row1" class="row_heading level0 row1" data-quarto-table-cell-role="th">1</td>
<td id="T_bb952_row1_col0" class="data row1 col0">Target</td>
<td id="T_bb952_row1_col1" class="data row1 col1">target</td>
</tr>
<tr class="odd">
<td id="T_bb952_level0_row2" class="row_heading level0 row2" data-quarto-table-cell-role="th">2</td>
<td id="T_bb952_row2_col0" class="data row2 col0">Target type</td>
<td id="T_bb952_row2_col1" class="data row2 col1">Binary</td>
</tr>
<tr class="even">
<td id="T_bb952_level0_row3" class="row_heading level0 row3" data-quarto-table-cell-role="th">3</td>
<td id="T_bb952_row3_col0" class="data row3 col0">Original data shape</td>
<td id="T_bb952_row3_col1" class="data row3 col1">(398, 31)</td>
</tr>
<tr class="odd">
<td id="T_bb952_level0_row4" class="row_heading level0 row4" data-quarto-table-cell-role="th">4</td>
<td id="T_bb952_row4_col0" class="data row4 col0">Transformed data shape</td>
<td id="T_bb952_row4_col1" class="data row4 col1">(398, 31)</td>
</tr>
<tr class="even">
<td id="T_bb952_level0_row5" class="row_heading level0 row5" data-quarto-table-cell-role="th">5</td>
<td id="T_bb952_row5_col0" class="data row5 col0">Transformed train set shape</td>
<td id="T_bb952_row5_col1" class="data row5 col1">(278, 31)</td>
</tr>
<tr class="odd">
<td id="T_bb952_level0_row6" class="row_heading level0 row6" data-quarto-table-cell-role="th">6</td>
<td id="T_bb952_row6_col0" class="data row6 col0">Transformed test set shape</td>
<td id="T_bb952_row6_col1" class="data row6 col1">(120, 31)</td>
</tr>
<tr class="even">
<td id="T_bb952_level0_row7" class="row_heading level0 row7" data-quarto-table-cell-role="th">7</td>
<td id="T_bb952_row7_col0" class="data row7 col0">Numeric features</td>
<td id="T_bb952_row7_col1" class="data row7 col1">30</td>
</tr>
<tr class="odd">
<td id="T_bb952_level0_row8" class="row_heading level0 row8" data-quarto-table-cell-role="th">8</td>
<td id="T_bb952_row8_col0" class="data row8 col0">Preprocess</td>
<td id="T_bb952_row8_col1" class="data row8 col1">True</td>
</tr>
<tr class="even">
<td id="T_bb952_level0_row9" class="row_heading level0 row9" data-quarto-table-cell-role="th">9</td>
<td id="T_bb952_row9_col0" class="data row9 col0">Imputation type</td>
<td id="T_bb952_row9_col1" class="data row9 col1">simple</td>
</tr>
<tr class="odd">
<td id="T_bb952_level0_row10" class="row_heading level0 row10" data-quarto-table-cell-role="th">10</td>
<td id="T_bb952_row10_col0" class="data row10 col0">Numeric imputation</td>
<td id="T_bb952_row10_col1" class="data row10 col1">mean</td>
</tr>
<tr class="even">
<td id="T_bb952_level0_row11" class="row_heading level0 row11" data-quarto-table-cell-role="th">11</td>
<td id="T_bb952_row11_col0" class="data row11 col0">Categorical imputation</td>
<td id="T_bb952_row11_col1" class="data row11 col1">mode</td>
</tr>
<tr class="odd">
<td id="T_bb952_level0_row12" class="row_heading level0 row12" data-quarto-table-cell-role="th">12</td>
<td id="T_bb952_row12_col0" class="data row12 col0">Fold Generator</td>
<td id="T_bb952_row12_col1" class="data row12 col1">StratifiedKFold</td>
</tr>
<tr class="even">
<td id="T_bb952_level0_row13" class="row_heading level0 row13" data-quarto-table-cell-role="th">13</td>
<td id="T_bb952_row13_col0" class="data row13 col0">Fold Number</td>
<td id="T_bb952_row13_col1" class="data row13 col1">10</td>
</tr>
<tr class="odd">
<td id="T_bb952_level0_row14" class="row_heading level0 row14" data-quarto-table-cell-role="th">14</td>
<td id="T_bb952_row14_col0" class="data row14 col0">CPU Jobs</td>
<td id="T_bb952_row14_col1" class="data row14 col1">-1</td>
</tr>
<tr class="even">
<td id="T_bb952_level0_row15" class="row_heading level0 row15" data-quarto-table-cell-role="th">15</td>
<td id="T_bb952_row15_col0" class="data row15 col0">Use GPU</td>
<td id="T_bb952_row15_col1" class="data row15 col1">False</td>
</tr>
<tr class="odd">
<td id="T_bb952_level0_row16" class="row_heading level0 row16" data-quarto-table-cell-role="th">16</td>
<td id="T_bb952_row16_col0" class="data row16 col0">Log Experiment</td>
<td id="T_bb952_row16_col1" class="data row16 col1">False</td>
</tr>
<tr class="even">
<td id="T_bb952_level0_row17" class="row_heading level0 row17" data-quarto-table-cell-role="th">17</td>
<td id="T_bb952_row17_col0" class="data row17 col0">Experiment Name</td>
<td id="T_bb952_row17_col1" class="data row17 col1">clf-default-name</td>
</tr>
<tr class="odd">
<td id="T_bb952_level0_row18" class="row_heading level0 row18" data-quarto-table-cell-role="th">18</td>
<td id="T_bb952_row18_col0" class="data row18 col0">USI</td>
<td id="T_bb952_row18_col1" class="data row18 col1">b51d</td>
</tr>
</tbody>
</table>
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text-align: left;
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#T_5bd4c_row0_col1, #T_5bd4c_row0_col5, #T_5bd4c_row0_col6, #T_5bd4c_row0_col7, #T_5bd4c_row9_col2, #T_5bd4c_row9_col4, #T_5bd4c_row15_col3 {
text-align: left;
background-color: yellow;
}
#T_5bd4c_row0_col8, #T_5bd4c_row1_col8, #T_5bd4c_row2_col8, #T_5bd4c_row3_col8, #T_5bd4c_row4_col8, #T_5bd4c_row5_col8, #T_5bd4c_row6_col8, #T_5bd4c_row7_col8, #T_5bd4c_row8_col8, #T_5bd4c_row9_col8, #T_5bd4c_row10_col8, #T_5bd4c_row11_col8, #T_5bd4c_row12_col8, #T_5bd4c_row13_col8, #T_5bd4c_row14_col8 {
text-align: left;
background-color: lightgrey;
}
#T_5bd4c_row15_col8 {
text-align: left;
background-color: yellow;
background-color: lightgrey;
}
</style>
<table id="T_5bd4c" data-quarto-postprocess="true">
<thead>
<tr class="header">
<th class="blank level0" data-quarto-table-cell-role="th"> </th>
<th id="T_5bd4c_level0_col0" class="col_heading level0 col0" data-quarto-table-cell-role="th">Model</th>
<th id="T_5bd4c_level0_col1" class="col_heading level0 col1" data-quarto-table-cell-role="th">Accuracy</th>
<th id="T_5bd4c_level0_col2" class="col_heading level0 col2" data-quarto-table-cell-role="th">AUC</th>
<th id="T_5bd4c_level0_col3" class="col_heading level0 col3" data-quarto-table-cell-role="th">Recall</th>
<th id="T_5bd4c_level0_col4" class="col_heading level0 col4" data-quarto-table-cell-role="th">Prec.</th>
<th id="T_5bd4c_level0_col5" class="col_heading level0 col5" data-quarto-table-cell-role="th">F1</th>
<th id="T_5bd4c_level0_col6" class="col_heading level0 col6" data-quarto-table-cell-role="th">Kappa</th>
<th id="T_5bd4c_level0_col7" class="col_heading level0 col7" data-quarto-table-cell-role="th">MCC</th>
<th id="T_5bd4c_level0_col8" class="col_heading level0 col8" data-quarto-table-cell-role="th">TT (Sec)</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td id="T_5bd4c_level0_row0" class="row_heading level0 row0" data-quarto-table-cell-role="th">lightgbm</td>
<td id="T_5bd4c_row0_col0" class="data row0 col0">Light Gradient Boosting Machine</td>
<td id="T_5bd4c_row0_col1" class="data row0 col1">0.9640</td>
<td id="T_5bd4c_row0_col2" class="data row0 col2">0.9877</td>
<td id="T_5bd4c_row0_col3" class="data row0 col3">0.9765</td>
<td id="T_5bd4c_row0_col4" class="data row0 col4">0.9681</td>
<td id="T_5bd4c_row0_col5" class="data row0 col5">0.9713</td>
<td id="T_5bd4c_row0_col6" class="data row0 col6">0.9227</td>
<td id="T_5bd4c_row0_col7" class="data row0 col7">0.9259</td>
<td id="T_5bd4c_row0_col8" class="data row0 col8">0.2660</td>
</tr>
<tr class="even">
<td id="T_5bd4c_level0_row1" class="row_heading level0 row1" data-quarto-table-cell-role="th">ridge</td>
<td id="T_5bd4c_row1_col0" class="data row1 col0">Ridge Classifier</td>
<td id="T_5bd4c_row1_col1" class="data row1 col1">0.9606</td>
<td id="T_5bd4c_row1_col2" class="data row1 col2">0.0000</td>
<td id="T_5bd4c_row1_col3" class="data row1 col3">0.9941</td>
<td id="T_5bd4c_row1_col4" class="data row1 col4">0.9471</td>
<td id="T_5bd4c_row1_col5" class="data row1 col5">0.9696</td>
<td id="T_5bd4c_row1_col6" class="data row1 col6">0.9132</td>
<td id="T_5bd4c_row1_col7" class="data row1 col7">0.9173</td>
<td id="T_5bd4c_row1_col8" class="data row1 col8">0.0100</td>
</tr>
<tr class="odd">
<td id="T_5bd4c_level0_row2" class="row_heading level0 row2" data-quarto-table-cell-role="th">rf</td>
<td id="T_5bd4c_row2_col0" class="data row2 col0">Random Forest Classifier</td>
<td id="T_5bd4c_row2_col1" class="data row2 col1">0.9569</td>
<td id="T_5bd4c_row2_col2" class="data row2 col2">0.9875</td>
<td id="T_5bd4c_row2_col3" class="data row2 col3">0.9827</td>
<td id="T_5bd4c_row2_col4" class="data row2 col4">0.9526</td>
<td id="T_5bd4c_row2_col5" class="data row2 col5">0.9661</td>
<td id="T_5bd4c_row2_col6" class="data row2 col6">0.9067</td>
<td id="T_5bd4c_row2_col7" class="data row2 col7">0.9120</td>
<td id="T_5bd4c_row2_col8" class="data row2 col8">0.0610</td>
</tr>
<tr class="even">
<td id="T_5bd4c_level0_row3" class="row_heading level0 row3" data-quarto-table-cell-role="th">xgboost</td>
<td id="T_5bd4c_row3_col0" class="data row3 col0">Extreme Gradient Boosting</td>
<td id="T_5bd4c_row3_col1" class="data row3 col1">0.9566</td>
<td id="T_5bd4c_row3_col2" class="data row3 col2">0.9905</td>
<td id="T_5bd4c_row3_col3" class="data row3 col3">0.9768</td>
<td id="T_5bd4c_row3_col4" class="data row3 col4">0.9564</td>
<td id="T_5bd4c_row3_col5" class="data row3 col5">0.9660</td>
<td id="T_5bd4c_row3_col6" class="data row3 col6">0.9060</td>
<td id="T_5bd4c_row3_col7" class="data row3 col7">0.9081</td>
<td id="T_5bd4c_row3_col8" class="data row3 col8">0.0330</td>
</tr>
<tr class="odd">
<td id="T_5bd4c_level0_row4" class="row_heading level0 row4" data-quarto-table-cell-role="th">lda</td>
<td id="T_5bd4c_row4_col0" class="data row4 col0">Linear Discriminant Analysis</td>
<td id="T_5bd4c_row4_col1" class="data row4 col1">0.9533</td>
<td id="T_5bd4c_row4_col2" class="data row4 col2">0.9882</td>
<td id="T_5bd4c_row4_col3" class="data row4 col3">0.9886</td>
<td id="T_5bd4c_row4_col4" class="data row4 col4">0.9420</td>
<td id="T_5bd4c_row4_col5" class="data row4 col5">0.9643</td>
<td id="T_5bd4c_row4_col6" class="data row4 col6">0.8964</td>
<td id="T_5bd4c_row4_col7" class="data row4 col7">0.9000</td>
<td id="T_5bd4c_row4_col8" class="data row4 col8">0.0100</td>
</tr>
<tr class="even">
<td id="T_5bd4c_level0_row5" class="row_heading level0 row5" data-quarto-table-cell-role="th">et</td>
<td id="T_5bd4c_row5_col0" class="data row5 col0">Extra Trees Classifier</td>
<td id="T_5bd4c_row5_col1" class="data row5 col1">0.9532</td>
<td id="T_5bd4c_row5_col2" class="data row5 col2">0.9876</td>
<td id="T_5bd4c_row5_col3" class="data row5 col3">0.9709</td>
<td id="T_5bd4c_row5_col4" class="data row5 col4">0.9562</td>
<td id="T_5bd4c_row5_col5" class="data row5 col5">0.9628</td>
<td id="T_5bd4c_row5_col6" class="data row5 col6">0.8994</td>
<td id="T_5bd4c_row5_col7" class="data row5 col7">0.9023</td>
<td id="T_5bd4c_row5_col8" class="data row5 col8">0.0460</td>
</tr>
<tr class="odd">
<td id="T_5bd4c_level0_row6" class="row_heading level0 row6" data-quarto-table-cell-role="th">catboost</td>
<td id="T_5bd4c_row6_col0" class="data row6 col0">CatBoost Classifier</td>
<td id="T_5bd4c_row6_col1" class="data row6 col1">0.9532</td>
<td id="T_5bd4c_row6_col2" class="data row6 col2">0.9876</td>
<td id="T_5bd4c_row6_col3" class="data row6 col3">0.9827</td>
<td id="T_5bd4c_row6_col4" class="data row6 col4">0.9460</td>
<td id="T_5bd4c_row6_col5" class="data row6 col5">0.9633</td>
<td id="T_5bd4c_row6_col6" class="data row6 col6">0.8987</td>
<td id="T_5bd4c_row6_col7" class="data row6 col7">0.9026</td>
<td id="T_5bd4c_row6_col8" class="data row6 col8">1.6130</td>
</tr>
<tr class="even">
<td id="T_5bd4c_level0_row7" class="row_heading level0 row7" data-quarto-table-cell-role="th">ada</td>
<td id="T_5bd4c_row7_col0" class="data row7 col0">Ada Boost Classifier</td>
<td id="T_5bd4c_row7_col1" class="data row7 col1">0.9530</td>
<td id="T_5bd4c_row7_col2" class="data row7 col2">0.9861</td>
<td id="T_5bd4c_row7_col3" class="data row7 col3">0.9595</td>
<td id="T_5bd4c_row7_col4" class="data row7 col4">0.9675</td>
<td id="T_5bd4c_row7_col5" class="data row7 col5">0.9624</td>
<td id="T_5bd4c_row7_col6" class="data row7 col6">0.8996</td>
<td id="T_5bd4c_row7_col7" class="data row7 col7">0.9029</td>
<td id="T_5bd4c_row7_col8" class="data row7 col8">0.0420</td>
</tr>
<tr class="odd">
<td id="T_5bd4c_level0_row8" class="row_heading level0 row8" data-quarto-table-cell-role="th">nb</td>
<td id="T_5bd4c_row8_col0" class="data row8 col0">Naive Bayes</td>
<td id="T_5bd4c_row8_col1" class="data row8 col1">0.9496</td>
<td id="T_5bd4c_row8_col2" class="data row8 col2">0.9877</td>
<td id="T_5bd4c_row8_col3" class="data row8 col3">0.9771</td>
<td id="T_5bd4c_row8_col4" class="data row8 col4">0.9465</td>
<td id="T_5bd4c_row8_col5" class="data row8 col5">0.9607</td>
<td id="T_5bd4c_row8_col6" class="data row8 col6">0.8904</td>
<td id="T_5bd4c_row8_col7" class="data row8 col7">0.8941</td>
<td id="T_5bd4c_row8_col8" class="data row8 col8">0.0120</td>
</tr>
<tr class="even">
<td id="T_5bd4c_level0_row9" class="row_heading level0 row9" data-quarto-table-cell-role="th">qda</td>
<td id="T_5bd4c_row9_col0" class="data row9 col0">Quadratic Discriminant Analysis</td>
<td id="T_5bd4c_row9_col1" class="data row9 col1">0.9459</td>
<td id="T_5bd4c_row9_col2" class="data row9 col2">0.9967</td>
<td id="T_5bd4c_row9_col3" class="data row9 col3">0.9307</td>
<td id="T_5bd4c_row9_col4" class="data row9 col4">0.9844</td>
<td id="T_5bd4c_row9_col5" class="data row9 col5">0.9551</td>
<td id="T_5bd4c_row9_col6" class="data row9 col6">0.8869</td>
<td id="T_5bd4c_row9_col7" class="data row9 col7">0.8939</td>
<td id="T_5bd4c_row9_col8" class="data row9 col8">0.0110</td>
</tr>
<tr class="odd">
<td id="T_5bd4c_level0_row10" class="row_heading level0 row10" data-quarto-table-cell-role="th">gbc</td>
<td id="T_5bd4c_row10_col0" class="data row10 col0">Gradient Boosting Classifier</td>
<td id="T_5bd4c_row10_col1" class="data row10 col1">0.9353</td>