Decision_Tree.py

import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
from sklearn.model_selection import GridSearchCV
import matplotlib.pyplot as plt
iris_data_set = pd.read_csv('C:\\Users\\zou\\Desktop\\ML\\decision_tree_data\\iris.csv')
x = iris_data_set.iloc[:, 0:4].values
y = iris_data_set.iloc[:, -1].values
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=1)
model = DecisionTreeClassifier()
param = {'criterion': ['gini', 'entropy'], 'max_depth': [30, 50, 60, 100], 'min_samples_leaf': [2, 3, 5, 10], 'min_impurity_decrease': [0.1, 0.2, 0.5]}
grid = GridSearchCV(model, param_grid=param, cv=5)
grid.fit(x_train, y_train)
print('最优分类器:', grid.best_estimator_)
print('最优超参数：', grid.best_params_)
print('最优分数:', grid.best_score_)
model = grid.best_estimator_
y_pre = model.predict(x_test)
print('正确标签：', y_test)
print('预测结果：', y_pre)
print('训练集分数：', model.score(x_train, y_train))
print('测试集分数：', model.score(x_test, y_test))
conf_mat = confusion_matrix(y_test, y_pre)
print('混淆矩阵：')
print(conf_mat)
print('分类指标报告：')
print(classification_report(y_test, y_pre))
print(model.feature_importances_)
fig = plt.figure()
ax = fig.add_subplot(111)
f1 = ax.scatter(list(range(len(x_test))), y_test, marker='*')
f2 = ax.scatter(list(range(len(x_test))), y_pre, marker='o')
plt.legend(handles=[f1, f2], labels=['True', 'Prediction'])
plt.show()