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)
interact(fit_randomized_tree, random_state=[0, 100])
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)
interact(fit_randomized_tree, random_state=[0, 100])
def plot_tree_interactive(X, y):
def interactive_tree(depth=5):
clf = DecisionTreeClassifier(max_depth=depth, random_state=0)
visualize_tree(clf, X, y)
return interact(interactive_tree, depth=[1, 5])
def plot_tree_interactive(X, y):
def interactive_tree(depth=5):
clf = DecisionTreeClassifier(max_depth=depth, random_state=0)
visualize_tree(clf, X, y)
return interact(interactive_tree, depth=[1, 5])
