决策树 分类模型 iris_dtree.py

import numpy as np
from sklearn import datasets
from sklearn.metrics import confusion_matrix,accuracy_score
from sklearn.model_selection import train_test_split
from sklearn import preprocessing

# 加载鸢尾花数据集
iris_X,iris_y = datasets.load_iris(return_X_y=True)
# 数据预处理：按列归一化
iris_X = preprocessing.scale(iris_X)
# 切分数据集：测试集 30%
iris_X_train,iris_X_test,iris_y_train,iris_y_test = train_test_split(iris_X,iris_y,test_size=0.3,random_state=0)
# 决策树 分类模型
from sklearn import tree
model = tree.DecisionTreeClassifier()
# 模型训练
model.fit(iris_X_train,iris_y_train)
# 模型预测
iris_y_pred = model.predict(iris_X_test)
# 模型评估
# 混淆矩阵
print(confusion_matrix(iris_y_test,iris_y_pred))
print("准确率: %.2f" % accuracy_score(iris_y_test,iris_y_pred))