📃 Solution for Exercise M5.01

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.

We first load the penguins dataset and split it into a training and a testing sets:

import pandas as pd

penguins = pd.read_csv("../datasets/penguins_classification.csv")
culmen_columns = ["Culmen Length (mm)", "Culmen Depth (mm)"]
target_column = "Species"

Note

If you want a deeper overview regarding this dataset, you can refer to the Appendix - Datasets description section at the end of this MOOC.

from sklearn.model_selection import train_test_split

data, target = penguins[culmen_columns], penguins[target_column]
data_train, data_test, target_train, target_test = train_test_split(
    data, target, random_state=0
)

Create a decision tree classifier with a maximum depth of 2 levels and fit the training data.

# solution
from sklearn.tree import DecisionTreeClassifier

tree = DecisionTreeClassifier(max_depth=2)
tree.fit(data_train, target_train)

DecisionTreeClassifier(max_depth=2)

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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.

# solution
import matplotlib.pyplot as plt
import matplotlib as mpl
import seaborn as sns

from sklearn.inspection import DecisionBoundaryDisplay


tab10_norm = mpl.colors.Normalize(vmin=-0.5, vmax=8.5)

palette = ["tab:blue", "tab:green", "tab:orange"]
DecisionBoundaryDisplay.from_estimator(
    tree,
    data_train,
    response_method="predict",
    cmap="tab10",
    norm=tab10_norm,
    alpha=0.5,
)
ax = sns.scatterplot(
    data=penguins,
    x=culmen_columns[0],
    y=culmen_columns[1],
    hue=target_column,
    palette=palette,
)
plt.legend(bbox_to_anchor=(1.05, 1), loc="upper left")
_ = plt.title("Decision boundary using a decision tree")

Did we make use of the feature “Culmen Length”? Plot the tree using the function sklearn.tree.plot_tree to find out!

# solution
from sklearn.tree import plot_tree

_, ax = plt.subplots(figsize=(16, 12))
_ = plot_tree(
    tree,
    feature_names=culmen_columns,
    class_names=tree.classes_.tolist(),
    impurity=False,
    ax=ax,
)

The resulting tree has 7 nodes: 3 of them are “split nodes” and 4 are “leaf nodes” (or simply “leaves”), organized in 2 levels. We see that the second tree level used the “Culmen Length” to make two new decisions. Qualitatively, we saw that such a simple tree was enough to classify the penguins’ species.

Compute the accuracy of the decision tree on the testing data.

# solution
test_score = tree.fit(data_train, target_train).score(data_test, target_test)
print(f"Accuracy of the DecisionTreeClassifier: {test_score:.2f}")

Accuracy of the DecisionTreeClassifier: 0.97

At this stage, we have the intuition that a decision tree is built by successively partitioning the feature space, considering one feature at a time.

We predict an Adelie penguin if the feature value is below the threshold, which is not surprising since this partition was almost pure. If the feature value is above the threshold, we predict the Gentoo penguin, the class that is most probable.

(Estimated) predicted probabilities in multi-class problems

For those interested, one can further try to visualize the output of predict_proba for a multiclass problem using DecisionBoundaryDisplay, except that for a K-class problem you have K probability outputs for each data point. Visualizing all these on a single plot can quickly become tricky to interpret. It is then common to instead produce K separate plots, one for each class, in a one-vs-rest (or one-vs-all) fashion.

For example, in the plot below, the first plot on the left shows in yellow the certainty on classifying a data point as belonging to the “Adelie” class. In the same plot, the spectre from green to purple represents the certainty of not belonging to the “Adelie” class. The same logic applies to the other plots in the figure.

import numpy as np

xx = np.linspace(30, 60, 100)
yy = np.linspace(10, 23, 100)
xx, yy = np.meshgrid(xx, yy)
Xfull = pd.DataFrame(
    {"Culmen Length (mm)": xx.ravel(), "Culmen Depth (mm)": yy.ravel()}
)

probas = tree.predict_proba(Xfull)
n_classes = len(np.unique(tree.classes_))

_, axs = plt.subplots(ncols=3, nrows=1, sharey=True, figsize=(12, 5))
plt.suptitle("Predicted probabilities for decision tree model", y=0.8)

for class_of_interest in range(n_classes):
    axs[class_of_interest].set_title(
        f"Class {tree.classes_[class_of_interest]}"
    )
    imshow_handle = axs[class_of_interest].imshow(
        probas[:, class_of_interest].reshape((100, 100)),
        extent=(30, 60, 10, 23),
        vmin=0.0,
        vmax=1.0,
        origin="lower",
        cmap="viridis",
    )
    axs[class_of_interest].set_xlabel("Culmen Length (mm)")
    if class_of_interest == 0:
        axs[class_of_interest].set_ylabel("Culmen Depth (mm)")
    idx = target_test == tree.classes_[class_of_interest]
    axs[class_of_interest].scatter(
        data_test["Culmen Length (mm)"].loc[idx],
        data_test["Culmen Depth (mm)"].loc[idx],
        marker="o",
        c="w",
        edgecolor="k",
    )

ax = plt.axes([0.15, 0.04, 0.7, 0.05])
plt.colorbar(imshow_handle, cax=ax, orientation="horizontal")
_ = plt.title("Probability")

Note

You may have noticed that we are no longer using a diverging colormap. Indeed, the chance level for a one-vs-rest binarization of the multi-class classification problem is almost never at predicted probability of 0.5. So using a colormap with a neutral white at 0.5 might give a false impression on the certainty.

In future versions of scikit-learn DecisionBoundaryDisplay will support a class_of_interest parameter that will allow in particular for a visualization of predict_proba in multi-class settings.

We also plan to make it possible to visualize the predict_proba values for the class with the maximum predicted probability (without having to pass a given a fixed class_of_interest value).