scikit-learn contains the DecisionTreeClassifier class, which can train a binary decision tree with Gini and cross-entropy impurity measures. In our example, let's consider a dataset with three features and three classes:

from sklearn.datasets import make_classification

>>> nb_samples = 500
>>> X, Y = make_classification(n_samples=nb_samples, n_features=3, n_informative=3, n_redundant=0, n_classes=3, n_clusters_per_class=1)

Let's first consider a classification with default Gini impurity:

from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import cross_val_score

>>> dt = DecisionTreeClassifier()
>>> print(cross_val_score(dt, X, Y, scoring='accuracy', cv=10).mean())
0.970

A very interesting feature is given by the possibility of exporting the tree in Graphviz format and converting it into a PDF.

To export a...