Notebook related to classification tree exercise

Author: ArturoAmorQ

notebooks/trees_ex_01.ipynb

@@ -6,16 +6,13 @@
    "source": [
     "# \ud83d\udcdd Exercise M5.01\n",
     "\n",
-    "In the previous notebook, we showed how a tree with a depth of 1 level was\n",
-    "working. The aim of this exercise is to repeat part of the previous experiment\n",
-    "for a depth with 2 levels to show how the process of partitioning is repeated\n",
-    "over time.\n",
+    "In the previous notebook, we showed how a tree with 1 level depth works. The\n",
+    "aim of this exercise is to repeat part of the previous experiment for a tree\n",
+    "with 2 levels depth to show how such parameter affects the feature space\n",
+    "partitioning.\n",
     "\n",
-    "Before to start, we will:\n",
-    "\n",
-    "* load the dataset;\n",
-    "* split the dataset into training and testing dataset;\n",
-    "* define the function to show the classification decision function."
+    "We first load the penguins dataset and split it into a training and a testing\n",
+    "sets:"
    ]
   },
   {
@@ -61,10 +58,35 @@
    "metadata": {},
    "source": [
     "Create a decision tree classifier with a maximum depth of 2 levels and fit the\n",
-    "training data. Once this classifier trained, plot the data and the decision\n",
-    "boundary to see the benefit of increasing the depth. To plot the decision\n",
-    "boundary, you should import the class `DecisionBoundaryDisplay` from the\n",
-    "module `sklearn.inspection` as shown in the previous course notebook."
+    "training data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Write your code here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now plot the data and the decision boundary of the trained classifier to see\n",
+    "the effect of increasing the depth of the tree.\n",
+    "\n",
+    "Hint: Use the class `DecisionBoundaryDisplay` from the module\n",
+    "`sklearn.inspection` as shown in previous course notebooks.\n",
+    "\n",
+    "<div class=\"admonition warning alert alert-danger\">\n",
+    "<p class=\"first admonition-title\" style=\"font-weight: bold;\">Warning</p>\n",
+    "<p class=\"last\">At this time, it is not possible to use <tt class=\"docutils literal\"><span class=\"pre\">response_method=\"predict_proba\"</span></tt> for\n",
+    "multiclass problems. This is a planned feature for a future version of\n",
+    "scikit-learn. In the mean time, you can use <tt class=\"docutils literal\"><span class=\"pre\">response_method=\"predict\"</span></tt>\n",
+    "instead.</p>\n",
+    "</div>"
    ]
   },
   {

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.