更新于
2026年7月11日
随机森林#
随机森林 (Random Forest) 是一种强大的监督式机器学习算法，属于集成学习（Ensemble Learning）中的 Bagging（装袋法）类别。它通过构建多棵决策树并将它们的预测结果合并来完成分类或回归任务，能有效减少单棵树容易出现的过度拟合（Overfitting）问题。
示例代码#
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
def load_data():
data = load_iris()
x, y = data.data, data.target
x_train, x_test, y_train, y_test \
= train_test_split(x, y, test_size=0.3,random_state=1)
ss = StandardScaler()
x_train = ss.fit_transform(x_train)
x_test = ss.transform(x_test)
return x_train, x_test, y_train, y_test
if __name__ == '__main__':
x_train, x_test, y_train, y_test = load_data()
model = RandomForestClassifier(n_estimators=2, max_features=3,
random_state=2)
model.fit(x_train, y_train)
print(model.score(x_test, y_test))运行结果#
0.9555555555555556
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