[ci skip] FIX Notebooks not updated by make notebooks (#743) 5cc989e

Author: ArturoAmorQ

_sources/python_scripts/trees_ex_01.py

@@ -14,16 +14,13 @@
 # %% [markdown]
 # # 📝 Exercise M5.01
 #
-# In the previous notebook, we showed how a tree with a depth of 1 level was
-# working. The aim of this exercise is to repeat part of the previous experiment
-# for a depth with 2 levels to show how the process of partitioning is repeated
-# over time.
+# In the previous notebook, we showed how a tree with 1 level depth works. The
+# aim of this exercise is to repeat part of the previous experiment for a tree
+# with 2 levels depth to show how such parameter affects the feature space
+# partitioning.
 #
-# Before to start, we will:
-#
-# * load the dataset;
-# * split the dataset into training and testing dataset;
-# * define the function to show the classification decision function.
+# We first load the penguins dataset and split it into a training and a testing
+# sets:
 
 # %%
 import pandas as pd
@@ -48,10 +45,24 @@
 
 # %% [markdown]
 # Create a decision tree classifier with a maximum depth of 2 levels and fit the
-# training data. Once this classifier trained, plot the data and the decision
-# boundary to see the benefit of increasing the depth. To plot the decision
-# boundary, you should import the class `DecisionBoundaryDisplay` from the
-# module `sklearn.inspection` as shown in the previous course notebook.
+# training data.
+
+# %%
+# Write your code here.
+
+# %% [markdown]
+# Now plot the data and the decision boundary of the trained classifier to see
+# the effect of increasing the depth of the tree.
+#
+# Hint: Use the class `DecisionBoundaryDisplay` from the module
+# `sklearn.inspection` as shown in previous course notebooks.
+#
+# ```{warning}
+# At this time, it is not possible to use `response_method="predict_proba"` for
+# multiclass problems. This is a planned feature for a future version of
+# scikit-learn. In the mean time, you can use `response_method="predict"`
+# instead.
+# ```
 
 # %%
 # Write your code here.

appendix/notebook_timings.html

@@ -1173,9 +1173,9 @@ <h1>Notebook timings<a class="headerlink" href="#notebook-timings" title="Permal
 <td><p>✅</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="xref doc reference internal" href="../python_scripts/trees_ex_01.html"><span class="doc">python_scripts/trees_ex_01</span></a></p></td>
-<td><p>2023-10-20 14:19</p></td>
+<td><p>2023-10-27 09:59</p></td>
 <td><p>cache</p></td>
-<td><p>2.0</p></td>
+<td><p>2.4</p></td>
 <td><p>✅</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="xref doc reference internal" href="../python_scripts/trees_ex_02.html"><span class="doc">python_scripts/trees_ex_02</span></a></p></td>

python_scripts/trees_ex_01.html

@@ -683,16 +683,12 @@ <h1>📝 Exercise M5.01</h1>
 
   <section class="tex2jax_ignore mathjax_ignore" id="exercise-m5-01">
 <h1>📝 Exercise M5.01<a class="headerlink" href="#exercise-m5-01" title="Permalink to this heading">#</a></h1>
-<p>In the previous notebook, we showed how a tree with a depth of 1 level was
-working. The aim of this exercise is to repeat part of the previous experiment
-for a depth with 2 levels to show how the process of partitioning is repeated
-over time.</p>
-<p>Before to start, we will:</p>
-<ul class="simple">
-<li><p>load the dataset;</p></li>
-<li><p>split the dataset into training and testing dataset;</p></li>
-<li><p>define the function to show the classification decision function.</p></li>
-</ul>
+<p>In the previous notebook, we showed how a tree with 1 level depth works. The
+aim of this exercise is to repeat part of the previous experiment for a tree
+with 2 levels depth to show how such parameter affects the feature space
+partitioning.</p>
+<p>We first load the penguins dataset and split it into a training and a testing
+sets:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
@@ -722,10 +718,25 @@ <h1>📝 Exercise M5.01<a class="headerlink" href="#exercise-m5-01" title="Perma
 </div>
 </div>
 <p>Create a decision tree classifier with a maximum depth of 2 levels and fit the
-training data. Once this classifier trained, plot the data and the decision
-boundary to see the benefit of increasing the depth. To plot the decision
-boundary, you should import the class <code class="docutils literal notranslate"><span class="pre">DecisionBoundaryDisplay</span></code> from the
-module <code class="docutils literal notranslate"><span class="pre">sklearn.inspection</span></code> as shown in the previous course notebook.</p>
+training data.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Write your code here.</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Now plot the data and the decision boundary of the trained classifier to see
+the effect of increasing the depth of the tree.</p>
+<p>Hint: Use the class <code class="docutils literal notranslate"><span class="pre">DecisionBoundaryDisplay</span></code> from the module
+<code class="docutils literal notranslate"><span class="pre">sklearn.inspection</span></code> as shown in previous course notebooks.</p>
+<div class="admonition warning">
+<p class="admonition-title">Warning</p>
+<p>At this time, it is not possible to use <code class="docutils literal notranslate"><span class="pre">response_method=&quot;predict_proba&quot;</span></code> for
+multiclass problems. This is a planned feature for a future version of
+scikit-learn. In the mean time, you can use <code class="docutils literal notranslate"><span class="pre">response_method=&quot;predict&quot;</span></code>
+instead.</p>
+</div>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Write your code here.</span>