[ci skip] FIX correction for some typos (#779)

Co-authored-by: ArturoAmorQ <[email protected]> 00379f8
INRIA · May 17, 2024 · bb625e6 · bb625e6
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diff --git a/_sources/python_scripts/cross_validation_ex_01.py b/_sources/python_scripts/cross_validation_ex_01.py
@@ -45,7 +45,7 @@
 # exercise.
 #
 # Also, this classifier can become more flexible/expressive by using a so-called
-# kernel that makes the model become non-linear. Again, no requirement regarding
+# kernel that makes the model become non-linear. Again, no undestanding regarding
 # the mathematics is required to accomplish this exercise.
 #
 # We will use an RBF kernel where a parameter `gamma` allows to tune the

diff --git a/_sources/python_scripts/cross_validation_sol_01.py b/_sources/python_scripts/cross_validation_sol_01.py
@@ -39,7 +39,7 @@
 # exercise.
 #
 # Also, this classifier can become more flexible/expressive by using a so-called
-# kernel that makes the model become non-linear. Again, no requirement regarding
+# kernel that makes the model become non-linear. Again, no understanding regarding
 # the mathematics is required to accomplish this exercise.
 #
 # We will use an RBF kernel where a parameter `gamma` allows to tune the

diff --git a/_sources/python_scripts/linear_regression_non_linear_link.py b/_sources/python_scripts/linear_regression_non_linear_link.py
@@ -37,7 +37,7 @@
 
 # %% [markdown]
 # ```{tip}
-# `np.random.RandomState` allows to create a random number generator which can
+# `np.random.RandomState` allows creating a random number generator which can
 # be later used to get deterministic results.
 # ```
 #
@@ -172,6 +172,7 @@ def fit_score_plot_regression(model, title=None):
 # of the absolute values of the differences between the features generated by
 # both methods and checking that it is close to zero:
 
+# %%
 np.abs(polynomial_expansion.fit_transform(data) - data_expanded).max()
 
 # %% [markdown]

diff --git a/appendix/notebook_timings.html b/appendix/notebook_timings.html
diff --git a/python_scripts/02_numerical_pipeline_cross_validation.html b/python_scripts/02_numerical_pipeline_cross_validation.html
@@ -799,12 +799,12 @@ <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 461 ms, sys: 273 ms, total: 734 ms
-Wall time: 412 ms
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 475 ms, sys: 251 ms, total: 726 ms
+Wall time: 411 ms
 </pre></div>
 </div>
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;fit_time&#39;: array([0.06219172, 0.05723834, 0.05901527, 0.05594277, 0.05530119]),
- &#39;score_time&#39;: array([0.02072406, 0.01340437, 0.01310992, 0.01340914, 0.01328731]),
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;fit_time&#39;: array([0.05962992, 0.05806112, 0.05891657, 0.05685925, 0.05641007]),
+ &#39;score_time&#39;: array([0.01352239, 0.0138371 , 0.01350832, 0.01330352, 0.01314974]),
  &#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
@@ -2067,7 +2067,7 @@ <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 0.130 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 0.134 seconds in 60 iterations
 </pre></div>
 </div>
 </div>

diff --git a/python_scripts/03_categorical_pipeline.html b/python_scripts/03_categorical_pipeline.html
@@ -2078,8 +2078,8 @@ <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([0.1831758 , 0.1687727 , 0.17786264, 0.18215322, 0.17073154]),
- &#39;score_time&#39;: array([0.02293634, 0.02322912, 0.02242661, 0.02430534, 0.02228951]),
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;fit_time&#39;: array([0.17973423, 0.16796279, 0.17482829, 0.17987633, 0.1681819 ]),
+ &#39;score_time&#39;: array([0.02228045, 0.02242637, 0.02157307, 0.02428603, 0.02156115]),
  &#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
@@ -1571,8 +1571,8 @@ <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([0.25394678, 0.25306678, 0.22307277, 0.24534893, 0.26477003]),
- &#39;score_time&#39;: array([0.02737975, 0.02651834, 0.03650928, 0.02847409, 0.0261445 ]),
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>{&#39;fit_time&#39;: array([0.25434685, 0.25564384, 0.22261763, 0.2406466 , 0.26352739]),
+ &#39;score_time&#39;: array([0.02670002, 0.02729535, 0.02718019, 0.028162  , 0.02679062]),
  &#39;test_score&#39;: array([0.85116184, 0.84993346, 0.8482801 , 0.85257985, 0.85544636])}
 </pre></div>
 </div>
@@ -1644,8 +1644,8 @@ <h2>Fitting a more powerful model<a class="headerlink" href="#fitting-a-more-pow
 </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 679 ms, sys: 11.4 ms, total: 690 ms
-Wall time: 690 ms
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>CPU times: user 669 ms, sys: 15.7 ms, total: 685 ms
+Wall time: 684 ms
 </pre></div>
 </div>
 </div>
@@ -1657,7 +1657,7 @@ <h2>Fitting a more powerful model<a class="headerlink" href="#fitting-a-more-pow
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.8801899926295963
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.8814183932519859
 </pre></div>
 </div>
 </div>

diff --git a/python_scripts/03_categorical_pipeline_ex_02.html b/python_scripts/03_categorical_pipeline_ex_02.html
@@ -782,7 +782,7 @@ <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 4.230
+<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 4.233
 </pre></div>
 </div>
 </div>

diff --git a/python_scripts/03_categorical_pipeline_sol_02.html b/python_scripts/03_categorical_pipeline_sol_02.html
@@ -788,7 +788,7 @@ <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 4.248
+<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 4.248
 </pre></div>
 </div>
 </div>
@@ -835,7 +835,7 @@ <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: 0.873 ± 0.003 with a fitting time of 4.267
+<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 4.258
 </pre></div>
 </div>
 </div>
@@ -891,7 +891,7 @@ <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 17.169
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>The mean cross-validation accuracy is: 0.873 ± 0.003 with a fitting time of 16.878
 </pre></div>
 </div>
 </div>

diff --git a/python_scripts/cross_validation_baseline.html b/python_scripts/cross_validation_baseline.html
@@ -743,13 +743,13 @@ <h1>Comparing model performance with a simple baseline<a class="headerlink" href
 </div>
 <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     45.637861
-std       1.170229
-min      43.175110
-25%      44.825677
-50%      45.639125
-75%      46.514792
-max      47.742172
+mean     45.738234
+std       1.202376
+min      43.087492
+25%      45.055968
+50%      45.764180
+75%      46.742279
+max      48.154477
 Name: Decision tree regressor, dtype: float64
 </pre></div>
 </div>
@@ -826,152 +826,152 @@ <h1>Comparing model performance with a simple baseline<a class="headerlink" href
   <tbody>
     <tr>
       <th>0</th>
-      <td>46.592386</td>
+      <td>46.762154</td>
       <td>90.713153</td>
     </tr>
     <tr>
       <th>1</th>
-      <td>46.523763</td>
+      <td>46.682655</td>
       <td>90.539353</td>
     </tr>
     <tr>
       <th>2</th>
-      <td>44.716731</td>
+      <td>44.507188</td>
       <td>91.941912</td>
     </tr>
     <tr>
       <th>3</th>
-      <td>43.891149</td>
+      <td>43.781727</td>
       <td>90.213912</td>
     </tr>
     <tr>
       <th>4</th>
-      <td>47.742172</td>
+      <td>48.154477</td>
       <td>92.015862</td>
     </tr>
     <tr>
       <th>5</th>
-      <td>44.830272</td>
+      <td>45.051056</td>
       <td>90.542490</td>
     </tr>
     <tr>
       <th>6</th>
-      <td>44.225642</td>
+      <td>44.244638</td>
       <td>89.757566</td>
     </tr>
     <tr>
       <th>7</th>
-      <td>44.824146</td>
+      <td>44.363026</td>
       <td>92.477244</td>
     </tr>
     <tr>
       <th>8</th>
-      <td>45.037734</td>
+      <td>45.222227</td>
       <td>90.947952</td>
     </tr>
     <tr>
       <th>9</th>
-      <td>44.225159</td>
+      <td>45.070704</td>
       <td>91.991373</td>
     </tr>
     <tr>
       <th>10</th>
-      <td>46.208717</td>
+      <td>46.800577</td>
       <td>92.023571</td>
     </tr>
     <tr>
       <th>11</th>
-      <td>45.656263</td>
+      <td>46.007645</td>
       <td>90.556965</td>
     </tr>
     <tr>
       <th>12</th>
-      <td>45.540615</td>
+      <td>45.688677</td>
       <td>91.539567</td>
     </tr>
     <tr>
       <th>13</th>
-      <td>45.686792</td>
+      <td>45.793358</td>
       <td>91.185225</td>
     </tr>
     <tr>
       <th>14</th>
-      <td>47.260021</td>
+      <td>46.854835</td>
       <td>92.298971</td>
     </tr>
     <tr>
       <th>15</th>
-      <td>44.144881</td>
+      <td>44.270414</td>
       <td>91.084639</td>
     </tr>
     <tr>
       <th>16</th>
-      <td>45.818771</td>
+      <td>46.170299</td>
       <td>90.984471</td>
     </tr>
     <tr>
       <th>17</th>
-      <td>46.824750</td>
+      <td>46.937056</td>
       <td>89.981744</td>
     </tr>
     <tr>
       <th>18</th>
-      <td>45.289211</td>
+      <td>45.097156</td>
       <td>90.547140</td>
     </tr>
     <tr>
       <th>19</th>
-      <td>47.158012</td>
+      <td>47.304524</td>
       <td>89.820219</td>
     </tr>
     <tr>
       <th>20</th>
-      <td>43.175110</td>
+      <td>43.087492</td>
       <td>91.768721</td>
     </tr>
     <tr>
       <th>21</th>
-      <td>46.167966</td>
+      <td>46.183518</td>
       <td>92.305556</td>
     </tr>
     <tr>
       <th>22</th>
-      <td>45.148490</td>
+      <td>45.664405</td>
       <td>90.503017</td>
     </tr>
     <tr>
       <th>23</th>
-      <td>46.797885</td>
+      <td>47.137156</td>
       <td>92.147974</td>
     </tr>
     <tr>
       <th>24</th>
-      <td>46.103920</td>
+      <td>45.735001</td>
       <td>91.386320</td>
     </tr>
     <tr>
       <th>25</th>
-      <td>45.613319</td>
+      <td>46.074533</td>
       <td>90.815660</td>
     </tr>
     <tr>
       <th>26</th>
-      <td>44.188829</td>
+      <td>44.417633</td>
       <td>92.216574</td>
     </tr>
     <tr>
       <th>27</th>
-      <td>46.487879</td>
+      <td>46.388824</td>
       <td>90.107460</td>
     </tr>
     <tr>
       <th>28</th>
-      <td>45.621987</td>
+      <td>45.299989</td>
       <td>90.620318</td>
     </tr>
     <tr>
       <th>29</th>
-      <td>47.633257</td>
+      <td>47.394084</td>
       <td>91.165331</td>
     </tr>
   </tbody>
@@ -992,7 +992,7 @@ <h1>Comparing model performance with a simple baseline<a class="headerlink" href
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/3d3fdf92d4180fd1d4ac7382ea70912f17e8915c8892514bc90d859e17f19ae7.png" src="../_images/3d3fdf92d4180fd1d4ac7382ea70912f17e8915c8892514bc90d859e17f19ae7.png" />
+<img alt="../_images/6dd818d378cd4cde2c0e154498070e9d1f841b9dbe65ef41b2eca50218bf5941.png" src="../_images/6dd818d378cd4cde2c0e154498070e9d1f841b9dbe65ef41b2eca50218bf5941.png" />
 </div>
 </div>
 <p>We see that the generalization performance of our decision tree is far from

diff --git a/python_scripts/cross_validation_ex_01.html b/python_scripts/cross_validation_ex_01.html
@@ -719,7 +719,7 @@ <h1>📝 Exercise M2.01<a class="headerlink" href="#exercise-m2-01" title="Perma
 linear model are different but we don’t need to know these details for the
 exercise.</p>
 <p>Also, this classifier can become more flexible/expressive by using a so-called
-kernel that makes the model become non-linear. Again, no requirement regarding
+kernel that makes the model become non-linear. Again, no undestanding regarding
 the mathematics is required to accomplish this exercise.</p>
 <p>We will use an RBF kernel where a parameter <code class="docutils literal notranslate"><span class="pre">gamma</span></code> allows to tune the
 flexibility of the model.</p>

diff --git a/python_scripts/cross_validation_grouping.html b/python_scripts/cross_validation_grouping.html
@@ -763,7 +763,7 @@ <h1>Sample grouping<a class="headerlink" href="#sample-grouping" title="Permalin
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>The average accuracy is 0.969 ± 0.010
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>The average accuracy is 0.961 ± 0.005
 </pre></div>
 </div>
 </div>
@@ -797,7 +797,7 @@ <h1>Sample grouping<a class="headerlink" href="#sample-grouping" title="Permalin
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/9943411be96f17a4d70f80b9fbf089e9966bd8a2a8d8d8c934762182642e641e.png" src="../_images/9943411be96f17a4d70f80b9fbf089e9966bd8a2a8d8d8c934762182642e641e.png" />
+<img alt="../_images/f174f19699ef9893f342cc3c89a0e6718cea4e6974cce70b919b3d39f32404e8.png" src="../_images/f174f19699ef9893f342cc3c89a0e6718cea4e6974cce70b919b3d39f32404e8.png" />
 </div>
 </div>
 <p>Shuffling the data results in a higher cross-validated test accuracy with less
@@ -1027,7 +1027,7 @@ <h1>Sample grouping<a class="headerlink" href="#sample-grouping" title="Permalin
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/909a6a8a536adcd2d3dad57baf26d7d839cd07268ae0d42a4656e68fde831f60.png" src="../_images/909a6a8a536adcd2d3dad57baf26d7d839cd07268ae0d42a4656e68fde831f60.png" />
+<img alt="../_images/80a414f11133c1cdfe1e0b28eeea7d6d2ac7a0764e99da9ce1595f5553a06d5b.png" src="../_images/80a414f11133c1cdfe1e0b28eeea7d6d2ac7a0764e99da9ce1595f5553a06d5b.png" />
 </div>
 </div>
 <p>In conclusion, accounting for any sample grouping patterns is crucial when

diff --git a/python_scripts/cross_validation_learning_curve.html b/python_scripts/cross_validation_learning_curve.html
@@ -794,7 +794,7 @@ <h2>Learning curve<a class="headerlink" href="#learning-curve" title="Permalink
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/4d876d94aee2719242808a6ed4e019522a6151a7593f327312c01968cdcc2def.png" src="../_images/4d876d94aee2719242808a6ed4e019522a6151a7593f327312c01968cdcc2def.png" />
+<img alt="../_images/b8da38bdb89f08d430c8be24bf1d24c06eb737c8cf1bea6a378081549c126ed7.png" src="../_images/b8da38bdb89f08d430c8be24bf1d24c06eb737c8cf1bea6a378081549c126ed7.png" />
 </div>
 </div>
 <p>Looking at the training error alone, we see that we get an error of 0 k$. It