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))