xlim=None, ylim=None, ax=None):

ax = ax or plt.gca()

# Plot the training points

ax.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap='viridis',

clim=(y.min(), y.max()), zorder=3)

ax.axis('tight')

ax.axis('off')

if xlim is None:

xlim = ax.get_xlim()

if ylim is None:

ylim = ax.get_ylim()

# fit the estimator

estimator.fit(X, y)

xx, yy = np.meshgrid(np.linspace(*xlim, num=200),

np.linspace(*ylim, num=200))

Z = estimator.predict(np.c_[xx.ravel(), yy.ravel()])

# Put the result into a color plot

n_classes = len(np.unique(y))

Z = Z.reshape(xx.shape)

contours = ax.contourf(xx, yy, Z, alpha=0.3,

levels=np.arange(n_classes + 1) - 0.5,

cmap='viridis', zorder=1)

ax.set(xlim=xlim, ylim=ylim)

# Plot the decision boundaries

def plot_boundaries(i, xlim, ylim):

if i >= 0:

tree = estimator.tree_

if tree.feature[i] == 0:

ax.plot([tree.threshold[i], tree.threshold[i]], ylim, '-k', zorder=2)

plot_boundaries(tree.children_left[i],

[xlim[0], tree.threshold[i]], ylim)

plot_boundaries(tree.children_right[i],

[tree.threshold[i], xlim[1]], ylim)

elif tree.feature[i] == 1:

ax.plot(xlim, [tree.threshold[i], tree.threshold[i]], '-k', zorder=2)

plot_boundaries(tree.children_left[i], xlim,

[ylim[0], tree.threshold[i]])

plot_boundaries(tree.children_right[i], xlim,

[tree.threshold[i], ylim[1]])

if boundaries:

def interactive_tree(depth=5):

clf = DecisionTreeClassifier(max_depth=depth, random_state=0)

visualize_tree(clf, X, y)

N = int(0.75 * X.shape[0])

xlim = (X[:, 0].min(), X[:, 0].max())

ylim = (X[:, 1].min(), X[:, 1].max())

def fit_randomized_tree(random_state=0):

clf = DecisionTreeClassifier(max_depth=15)

i = np.arange(len(y))

rng = np.random.RandomState(random_state)

rng.shuffle(i)

visualize_tree(clf, X[i[:N]], y[i[:N]], boundaries=False,

xlim=xlim, ylim=ylim)