from main import mglearn, np, plt, train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_moons
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=0)
forest = RandomForestClassifier(n_estimators=5, random_state=2)
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()
from main import mglearn, train_test_split
from sklearn.linear_model import Ridge
X, y = mglearn.datasets.make_wave(n_samples=60)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
ridge = Ridge().fit(X_train, y_train)
print("Training set score: {:.2f}".format(ridge.score(X_train, y_train)))
print("Test set score: {:.2f}".format(ridge.score(X_test, y_test)))
from main import mglearn, np, plt, train_test_split
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.datasets import load_breast_cancer
cancer = load_breast_cancer()
X_train, X_test, y_train, y_test = train_test_split(cancer['data'], cancer['target'], random_state=0)
gbrt = GradientBoostingClassifier(random_state=0, max_depth=1)
gbrt.fit(X_train, y_train)
print("{:.3f}".format(gbrt.score(X_train, y_train)))
print("{:.3f}".format(gbrt.score(X_test, y_test)))