DAY 16
0

## ML_Day16(決策樹(Decision Tree))

• Gini 不純度(Gini impurity): p(i|t)代表在某個節點t，屬於類別c的比例。

• 熵(entropy): 不確定性的多少，假如在做二元分類時，如果p(i=1|t) = 1或p(i=0|t) = 0

• 分類錯誤率(classification error):

1. Gini 不純度(Gini impurity):
2. 熵(entropy):

3. 分類錯誤率(classification error):

``````import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets, metrics
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
from sklearn.model_selection import train_test_split
from matplotlib.colors import ListedColormap

def plot_decision_regions(X, y, classifier, test_idx=None, resolution=0.02):
# setup markers generator and color map
markers = ('s', 'x', 'o', '^', 'v')
colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')
cmap = ListedColormap(colors[:len(np.unique(y))])

# plot the decision surface
x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1
x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx1, xx2 =  np.meshgrid(np.arange(x1_min, x1_max, resolution), np.arange(x2_min, x2_max, resolution))

z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)
z = z.reshape(xx1.shape)
plt.contourf(xx1, xx2, z, alpha=0.4, cmap=cmap)
plt.xlim(xx1.min(), xx1.max())
plt.ylim(xx2.min(), xx2.max())

# plot all samples
X_test, y_test = X[test_idx, :], y[test_idx]
for idx, cl in enumerate(np.unique(y)):
plt.scatter(x=X[y==cl, 0], y=X[y==cl, 1], alpha=0.8, c=cmap(idx), marker=markers[idx], label=cl)

# hightlight test samples
if test_idx:
X_test, y_test = X[test_idx, :], y[test_idx]
plt.scatter(X_test[:, 0], X_test[:, 1], c='', alpha=1.0, linewidth=1, marker='o', s=55, label='test set')

def main():
x_train, x_test, y_train, y_test = train_test_split(iris.data[:, [2, 3]], iris.target, test_size=0.25, random_state=4)
clf = DecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=0)
clf.fit(x_train, y_train)
y_pred = clf.predict(x_test)

X_combined = np.vstack((x_train, x_test))
y_combined = np.hstack((y_train, y_test))

plot_decision_regions(X_combined, y_combined, classifier=clf)
plt.xlabel('petal length [cm]')
plt.ylabel('petal width [cm]')
plt.legend(loc='upper left')
plt.show()

if __name__ == '__main__':
main()
``````