决策树代码实践

喜欢这种用手敲敲打打的感觉~~~

一，代码

import numpy as np
import matplotlib.pyplot as plt
import graphviz
from sklearn.tree import export_graphviz
from matplotlib.colors import ListedColormap
from sklearn import tree, datasets
from sklearn.model_selection import train_test_split

wine = datasets.load_wine()
X, y = wine.data[:, :2], wine.target
X_train, X_test, y_train, y_test = train_test_split(X, y)

clf = tree.DecisionTreeClassifier(max_depth=3)
clf.fit(X_train, y_train)

# 定义图像中分区的颜色和散点的颜色
cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])
cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])
# 分别用样本的两个特征值创建图像的横轴和纵轴
x_min, x_max = X_train[:, 0].min() -1, X_train[:, 0].max() + 1
y_min, y_max = X_train[:, 1].min() -1, X_train[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, .02),
                     np.arange(y_min, y_max, .02))
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
# 给每个分类中的样本分配 不同的颜色
Z = Z.reshape(xx.shape)
plt.figure()
plt.pcolormesh(xx, yy, Z, cmap=cmap_light)
#用散点把样本表示出来
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold, edgecolors='k', s=20)
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.title('Classifier: (max_depth = 3)')
plt.show()

export_graphviz(clf, out_file='wine.dot', class_names=wine.target_names,
                feature_names=wine.feature_names[:2], impurity=False, filled=True)
with open('wine.dot') as f:
    dot_graph = f.read()
    print(graphviz.Source(dot_graph))

二，效果

2022-04-26 08_59_32-MessageCenterUI.png