数据科学导论决策树分类模型

最新推荐文章于 2025-11-29 22:41:10 发布

原创最新推荐文章于 2025-11-29 22:41:10 发布 · 446 阅读

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#决策树 #分类 #机器学习

本文展示了如何使用Python的scikit-learn库对Iris数据集进行决策树分类，包括数据预处理、模型训练、测试集评估和性能报告生成。

from sklearn import datasets
from sklearn import tree
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
tree = tree.DecisionTreeClassifier(criterion= 'entropy')
iris = datasets.load_iris()
X=iris.data
Y=iris.target
x_train, x_test, y_train,y_test=train_test_split(X,Y,test_size=0.3,random_state=0)
print ("The iris target names: %s"% (iris.target_names))
tree.fit(x_train, y_train)
print("决策树模型训练集的准确率：%.3f" %tree.score(x_train, y_train))
print('决策树模型测试集的谁确率：% 3f' %tree.score(x_test,y_test))
target_names = ['setosa','versicolor','virginica']
y_hat = tree.predict(x_test)
print(classification_report(y_test, y_hat, target_names = target_names))