[ci skip] remove dublicate word (#783) 16938a6

INRIA · Dec 6, 2024 · 5c231aa · 5c231aa
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commit 5c231aa
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diff --git a/_sources/python_scripts/parameter_tuning_grid_search.py b/_sources/python_scripts/parameter_tuning_grid_search.py
@@ -148,7 +148,7 @@
 # cross-validation by providing `model_grid_search` as a model to the
 # `cross_validate` function.
 #
-# Here, we used a single train-test split to to evaluate `model_grid_search`. In
+# Here, we used a single train-test split to evaluate `model_grid_search`. In
 # a future notebook will go into more detail about nested cross-validation, when
 # you use cross-validation both for hyperparameter tuning and model evaluation.
 # ```

diff --git a/appendix/notebook_timings.html b/appendix/notebook_timings.html
diff --git a/python_scripts/01_tabular_data_exploration.html b/python_scripts/01_tabular_data_exploration.html
@@ -1242,7 +1242,7 @@ <h2>Visual inspection of the data<a class="headerlink" href="#visual-inspection-
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/b244e4c5111beac6072e0d539fd03b2822147338599ae43cfbb4412707cae4f2.png" src="../_images/b244e4c5111beac6072e0d539fd03b2822147338599ae43cfbb4412707cae4f2.png" />
+<img alt="../_images/7a7f88bdf819300be499d33045b2468420dc816dd9baff93a0f4f701a5e33d41.png" src="../_images/7a7f88bdf819300be499d33045b2468420dc816dd9baff93a0f4f701a5e33d41.png" />
 </div>
 </div>
 <div class="admonition tip">
@@ -1742,7 +1742,7 @@ <h2>Visual inspection of the data<a class="headerlink" href="#visual-inspection-
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/0650da8770d65a42d589710a45af4f7ca1f96cf7fee7f4b63c59b8c123a0aa00.png" src="../_images/0650da8770d65a42d589710a45af4f7ca1f96cf7fee7f4b63c59b8c123a0aa00.png" />
+<img alt="../_images/4480f1bfb429d0aa33dbd6816b3187263868d745e9deaedb7b1bcf313bfef4dd.png" src="../_images/4480f1bfb429d0aa33dbd6816b3187263868d745e9deaedb7b1bcf313bfef4dd.png" />
 </div>
 </div>
 </section>
@@ -1764,7 +1764,7 @@ <h2>Creating decision rules by hand<a class="headerlink" href="#creating-decisio
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/10ceefb193108e7a217e46906c07afdacffc8f36a90f7bc25920f7f7252c0f1d.png" src="../_images/10ceefb193108e7a217e46906c07afdacffc8f36a90f7bc25920f7f7252c0f1d.png" />
+<img alt="../_images/7a467aafb50d1f6391aff3f29d702908d772f86d8664de99714416a47d87e76c.png" src="../_images/7a467aafb50d1f6391aff3f29d702908d772f86d8664de99714416a47d87e76c.png" />
 </div>
 </div>
 <p>The data points (circles) show the distribution of <code class="docutils literal notranslate"><span class="pre">&quot;hours-per-week&quot;</span></code> and
@@ -1801,7 +1801,7 @@ <h2>Creating decision rules by hand<a class="headerlink" href="#creating-decisio
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/34027d88cb67e02e484a55734769432dc305899b3f33fcfea3e73142ebf8a7ad.png" src="../_images/34027d88cb67e02e484a55734769432dc305899b3f33fcfea3e73142ebf8a7ad.png" />
+<img alt="../_images/eb44e87e66819a6c593df6f2c5f454c5eeded4e05505198cd2bc4777efe8cd6f.png" src="../_images/eb44e87e66819a6c593df6f2c5f454c5eeded4e05505198cd2bc4777efe8cd6f.png" />
 </div>
 </div>
 <ul class="simple">

diff --git a/python_scripts/01_tabular_data_exploration_sol_01.html b/python_scripts/01_tabular_data_exploration_sol_01.html
@@ -809,7 +809,7 @@ <h1>📃 Solution for Exercise M1.01<a class="headerlink" href="#solution-for-ex
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-<img alt="../_images/e624ec6869b2b90d339778bc82f5e93985791467e44fa832ae432553a115aa1c.png" src="../_images/e624ec6869b2b90d339778bc82f5e93985791467e44fa832ae432553a115aa1c.png" />
+<img alt="../_images/eb40b79917dc4278928768ba6ccda48beabb4585114f5df67c144eec550aa978.png" src="../_images/eb40b79917dc4278928768ba6ccda48beabb4585114f5df67c144eec550aa978.png" />
 </div>
 </div>
 <p>Show features distribution for each class. Hint: use
@@ -824,7 +824,7 @@ <h1>📃 Solution for Exercise M1.01<a class="headerlink" href="#solution-for-ex
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/9771cac72922ac50c1c5ff5efee2dc80a8358557281ac33acfac4e9901afdab4.png" src="../_images/9771cac72922ac50c1c5ff5efee2dc80a8358557281ac33acfac4e9901afdab4.png" />
+<img alt="../_images/815d33059426f77b7725353017ed6c5c333b9d122d487c5eff808a715a9090bc.png" src="../_images/815d33059426f77b7725353017ed6c5c333b9d122d487c5eff808a715a9090bc.png" />
 </div>
 </div>
 <p>We observe that the labels on the axis are overlapping. Even if it is not the
@@ -837,7 +837,7 @@ <h1>📃 Solution for Exercise M1.01<a class="headerlink" href="#solution-for-ex
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/db326a45051e4221ef46a03e0b35384c60a1e44ebbc9487b5d827ba4700cd801.png" src="../_images/db326a45051e4221ef46a03e0b35384c60a1e44ebbc9487b5d827ba4700cd801.png" />
+<img alt="../_images/6f3067cdaa62548b2db8b30ed496a613bc74d87996b35adef5d353b83832dee2.png" src="../_images/6f3067cdaa62548b2db8b30ed496a613bc74d87996b35adef5d353b83832dee2.png" />
 </div>
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 <p>Looking at these distributions, how hard do you think it would be to classify

diff --git a/python_scripts/02_numerical_pipeline_cross_validation.html b/python_scripts/02_numerical_pipeline_cross_validation.html
@@ -802,12 +802,12 @@ <h2>The need for cross-validation<a class="headerlink" href="#the-need-for-cross
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 408 ms, sys: 4.32 ms, total: 412 ms
-Wall time: 412 ms
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 415 ms, sys: 7.35 ms, total: 422 ms
+Wall time: 422 ms
 </pre></div>
 </div>
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;fit_time&#39;: array([0.06166363, 0.05808997, 0.0588975 , 0.05876946, 0.05699682]),
- &#39;score_time&#39;: array([0.01387858, 0.01366115, 0.01383376, 0.01335287, 0.01387286]),
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;fit_time&#39;: array([0.06349206, 0.06000161, 0.06087375, 0.05924511, 0.05910063]),
+ &#39;score_time&#39;: array([0.01346612, 0.01429081, 0.01417804, 0.01412201, 0.01388645]),
  &#39;test_score&#39;: array([0.79557785, 0.80049135, 0.79965192, 0.79873055, 0.80456593])}
 </pre></div>
 </div>

diff --git a/python_scripts/02_numerical_pipeline_scaling.html b/python_scripts/02_numerical_pipeline_scaling.html
@@ -1534,8 +1534,8 @@ <h2>Model fitting with preprocessing<a class="headerlink" href="#model-fitting-w
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 </div>
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-<img alt="../_images/2d737418a4078855e6e8aa588125c39822c85265cbdba24f44730c329be7dffe.png" src="../_images/2d737418a4078855e6e8aa588125c39822c85265cbdba24f44730c329be7dffe.png" />
-<img alt="../_images/2b89041307f5896e5bef32520e6765b023cb6052719809b3f88722f72162b9df.png" src="../_images/2b89041307f5896e5bef32520e6765b023cb6052719809b3f88722f72162b9df.png" />
+<img alt="../_images/6d10e4645f73f5f26bcf23448bf5cad52303b15a23d39faa6f9c3e731cefb183.png" src="../_images/6d10e4645f73f5f26bcf23448bf5cad52303b15a23d39faa6f9c3e731cefb183.png" />
+<img alt="../_images/135235fe7bed34ce0ace810b90dc03a4471df58d4594778c93358c2c253fd2a2.png" src="../_images/135235fe7bed34ce0ace810b90dc03a4471df58d4594778c93358c2c253fd2a2.png" />
 </div>
 </div>
 <p>We can easily combine sequential operations with a scikit-learn <code class="docutils literal notranslate"><span class="pre">Pipeline</span></code>,
@@ -2070,7 +2070,7 @@ <h2>Model fitting with preprocessing<a class="headerlink" href="#model-fitting-w
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>The accuracy using a LogisticRegression is 0.807 with a fitting time of 0.124 seconds in 60 iterations
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>The accuracy using a LogisticRegression is 0.807 with a fitting time of 0.126 seconds in 60 iterations
 </pre></div>
 </div>
 </div>

diff --git a/python_scripts/03_categorical_pipeline.html b/python_scripts/03_categorical_pipeline.html
@@ -2081,8 +2081,8 @@ <h2>Evaluate our predictive pipeline<a class="headerlink" href="#evaluate-our-pr
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;fit_time&#39;: array([0.18256974, 0.17097569, 0.17535949, 0.18373227, 0.16953468]),
- &#39;score_time&#39;: array([0.02310443, 0.02300811, 0.02338481, 0.02385187, 0.02375031]),
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;fit_time&#39;: array([0.18743944, 0.17551827, 0.17746329, 0.18030071, 0.17144823]),
+ &#39;score_time&#39;: array([0.02368689, 0.02380896, 0.0232861 , 0.02382231, 0.02246022]),
  &#39;test_score&#39;: array([0.83232675, 0.83570478, 0.82831695, 0.83292383, 0.83497133])}
 </pre></div>
 </div>

diff --git a/python_scripts/03_categorical_pipeline_column_transformer.html b/python_scripts/03_categorical_pipeline_column_transformer.html
@@ -1574,8 +1574,8 @@ <h2>Evaluation of the model with cross-validation<a class="headerlink" href="#ev
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;fit_time&#39;: array([0.25424695, 0.25436568, 0.22307348, 0.24186993, 0.2620666 ]),
- &#39;score_time&#39;: array([0.02859521, 0.0277586 , 0.02748656, 0.02876139, 0.02706432]),
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;fit_time&#39;: array([0.25551748, 0.25741863, 0.22249246, 0.24788809, 0.26372337]),
+ &#39;score_time&#39;: array([0.02870536, 0.02681351, 0.02664328, 0.02990031, 0.02677202]),
  &#39;test_score&#39;: array([0.85116184, 0.84993346, 0.8482801 , 0.85257985, 0.85544636])}
 </pre></div>
 </div>
@@ -1647,8 +1647,8 @@ <h2>Fitting a more powerful model<a class="headerlink" href="#fitting-a-more-pow
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 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 626 ms, sys: 11.9 ms, total: 638 ms
-Wall time: 638 ms
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 656 ms, sys: 118 µs, total: 656 ms
+Wall time: 655 ms
 </pre></div>
 </div>
 </div>
@@ -1660,7 +1660,7 @@ <h2>Fitting a more powerful model<a class="headerlink" href="#fitting-a-more-pow
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 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.8787159118827287
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.8797805257554664
 </pre></div>
 </div>
 </div>

diff --git a/python_scripts/03_categorical_pipeline_ex_02.html b/python_scripts/03_categorical_pipeline_ex_02.html
@@ -785,7 +785,7 @@ <h2>Reference pipeline (no numerical scaling and integer-coded categories)<a cla
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>The mean cross-validation accuracy is: 0.873 ± 0.002 with a fitting time of 3.982
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>The mean cross-validation accuracy is: 0.874 ± 0.003 with a fitting time of 4.021
 </pre></div>
 </div>
 </div>

diff --git a/python_scripts/03_categorical_pipeline_sol_02.html b/python_scripts/03_categorical_pipeline_sol_02.html
@@ -791,7 +791,7 @@ <h2>Reference pipeline (no numerical scaling and integer-coded categories)<a cla
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>The mean cross-validation accuracy is: 0.873 ± 0.002 with a fitting time of 4.000
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>The mean cross-validation accuracy is: 0.873 ± 0.002 with a fitting time of 4.047
 </pre></div>
 </div>
 </div>
@@ -838,7 +838,7 @@ <h2>Scaling numerical features<a class="headerlink" href="#scaling-numerical-fea
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>The mean cross-validation accuracy is: 0.872 ± 0.002 with a fitting time of 3.947
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>The mean cross-validation accuracy is: 0.874 ± 0.002 with a fitting time of 4.054
 </pre></div>
 </div>
 </div>
@@ -894,7 +894,7 @@ <h2>One-hot encoding of categorical variables<a class="headerlink" href="#one-ho
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>The mean cross-validation accuracy is: 0.873 ± 0.002 with a fitting time of 16.217
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>The mean cross-validation accuracy is: 0.874 ± 0.002 with a fitting time of 16.471
 </pre></div>
 </div>
 </div>

diff --git a/python_scripts/cross_validation_baseline.html b/python_scripts/cross_validation_baseline.html
@@ -746,13 +746,13 @@ <h1>Comparing model performance with a simple baseline<a class="headerlink" href
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 <div class="cell_output docutils container">
 <div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>count    30.000000
-mean     45.789573
-std       1.230836
-min      42.939234
-25%      44.946287
-50%      45.810916
-75%      46.625877
-max      48.026353
+mean     45.709375
+std       1.270852
+min      42.790252
+25%      45.033623
+50%      45.646700
+75%      46.694564
+max      48.084202
 Name: Decision tree regressor, dtype: float64
 </pre></div>
 </div>
@@ -829,152 +829,152 @@ <h1>Comparing model performance with a simple baseline<a class="headerlink" href
   <tbody>
     <tr>
       <th>0</th>
-      <td>46.674628</td>
+      <td>47.336889</td>
       <td>90.713153</td>
     </tr>
     <tr>
       <th>1</th>
-      <td>46.787644</td>
+      <td>47.115550</td>
       <td>90.539353</td>
     </tr>
     <tr>
       <th>2</th>
-      <td>44.500138</td>
+      <td>44.110407</td>
       <td>91.941912</td>
     </tr>
     <tr>
       <th>3</th>
-      <td>43.408689</td>
+      <td>43.620679</td>
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     <tr>
       <th>4</th>
-      <td>48.026353</td>
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     </tr>
     <tr>
       <th>5</th>
-      <td>45.504401</td>
+      <td>45.182018</td>
       <td>90.542490</td>
     </tr>
     <tr>
       <th>6</th>
-      <td>44.208422</td>
+      <td>43.651443</td>
       <td>89.757566</td>
     </tr>
     <tr>
       <th>7</th>
-      <td>44.883667</td>
+      <td>44.985603</td>
       <td>92.477244</td>
     </tr>
     <tr>
       <th>8</th>
-      <td>45.134149</td>
+      <td>45.361547</td>
       <td>90.947952</td>
     </tr>
     <tr>
       <th>9</th>
-      <td>45.241490</td>
+      <td>44.732816</td>
       <td>91.991373</td>
     </tr>
     <tr>
       <th>10</th>
-      <td>46.479623</td>
+      <td>46.738223</td>
       <td>92.023571</td>
     </tr>
     <tr>
       <th>11</th>
-      <td>46.309001</td>
+      <td>46.003719</td>
       <td>90.556965</td>
     </tr>
     <tr>
       <th>12</th>
-      <td>45.690299</td>
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       <td>91.539567</td>
     </tr>
     <tr>
       <th>13</th>
-      <td>45.679379</td>
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       <td>91.185225</td>
     </tr>
     <tr>
       <th>14</th>
-      <td>47.520800</td>
+      <td>46.936813</td>
       <td>92.298971</td>
     </tr>
     <tr>
       <th>15</th>
-      <td>44.331630</td>
+      <td>44.902055</td>
       <td>91.084639</td>
     </tr>
     <tr>
       <th>16</th>
-      <td>46.235730</td>
+      <td>45.594232</td>
       <td>90.984471</td>
     </tr>
     <tr>
       <th>17</th>
-      <td>46.983591</td>
+      <td>47.081846</td>
       <td>89.981744</td>
     </tr>
     <tr>
       <th>18</th>
-      <td>44.820654</td>
+      <td>45.177681</td>
       <td>90.547140</td>
     </tr>
     <tr>
       <th>19</th>
-      <td>47.076617</td>
+      <td>47.094761</td>
       <td>89.820219</td>
     </tr>
     <tr>
       <th>20</th>
-      <td>42.939234</td>
+      <td>42.790252</td>
       <td>91.768721</td>
     </tr>
     <tr>
       <th>21</th>
-      <td>45.704958</td>
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     </tr>
     <tr>
       <th>22</th>
-      <td>45.896213</td>
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       <td>90.503017</td>
     </tr>
     <tr>
       <th>23</th>
-      <td>46.750189</td>
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       <td>92.147974</td>
     </tr>
     <tr>
       <th>24</th>
-      <td>46.361768</td>
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       <td>91.386320</td>
     </tr>
     <tr>
       <th>25</th>
-      <td>45.919011</td>
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       <td>90.815660</td>
     </tr>
     <tr>
       <th>26</th>
-      <td>44.668147</td>
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       <td>92.216574</td>
     </tr>
     <tr>
       <th>27</th>
-      <td>46.274482</td>
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       <td>90.107460</td>
     </tr>
     <tr>
       <th>28</th>
-      <td>45.725620</td>
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       <td>90.620318</td>
     </tr>
     <tr>
       <th>29</th>
-      <td>47.950674</td>
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       <td>91.165331</td>
     </tr>
   </tbody>
@@ -995,7 +995,7 @@ <h1>Comparing model performance with a simple baseline<a class="headerlink" href
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/46fc56f3f879b8df9702d7a53dce394d8879fbe0d557ace586d2c361fb48efcc.png" src="../_images/46fc56f3f879b8df9702d7a53dce394d8879fbe0d557ace586d2c361fb48efcc.png" />
+<img alt="../_images/9afa0b40290368120da474bf558fd3ef7133287428bab302c4472a0dfbe399ad.png" src="../_images/9afa0b40290368120da474bf558fd3ef7133287428bab302c4472a0dfbe399ad.png" />
 </div>
 </div>
 <p>We see that the generalization performance of our decision tree is far from