randomForestsTwoMoons.py

from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_moons
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import mglearn
# playing about with random forests
# The randomisation in data selection (with replacement) provides different perspectives
X, y = make_moons(n_samples=100, noise=0.25, random_state=3)
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=42)
# Forest consisting of 5 trees
forest = RandomForestClassifier(n_estimators=5, random_state=2)
# Random state for reproducable results
forest.fit(X_train, y_train)

fig, axes = plt.subplots(2,3,figsize=(20,10))
for i, (ax,tree) in enumerate(zip(axes.ravel(), forest.estimators_)):
    ax.set_title("Tree {}".format(i))
    mglearn.plots.plot_tree_partition(X_train, y_train, tree, ax=ax)

mglearn.plots.plot_2d_separator(forest, X_train, fill=True, ax=axes[-1,-1],alpha=.4)
axes[-1,-1].set_title("Random Forest")

mglearn.discrete_scatter(X_train[:, 0], X_train[:, 1], y_train)

plt.show()