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