在Python中，可以使用`scikit-learn`库来实现随机森林。随机森林是基于多颗决策树的一种集成学习算法，可用于分类和回归任务[^4]。 ### 分类任务 ```python from sklearn.ensemble import RandomForestClassifier from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score # 生成示例数据 X, y = make_classification(n_samples=1000, n_features=10, n_informative=5, n_redundant=0, random_state=42) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 创建随机森林分类器 clf = RandomForestClassifier(n_estimators=100, random_state=42) # 训练模型 clf.fit(X_train, y_train) # 进行预测 y_pred = clf.predict(X_test) # 计算准确率 accuracy = accuracy_score(y_test, y_pred) print(f"Accuracy: {accuracy}") ``` ### 回归任务 ```python from sklearn.ensemble import RandomForestRegressor from sklearn.datasets import make_regression from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error # 生成示例数据 X, y = make_regression(n_samples=1000, n_features=10, n_informative=5, random_state=42) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 创建随机森林回归器 reg = RandomForestRegressor(n_estimators=100, random_state=42) # 训练模型 reg.fit(X_train, y_train) # 进行预测 y_pred = reg.predict(X_test) # 计算均方误差 mse = mean_squared_error(y_test, y_pred) print(f"Mean Squared Error: {mse}") ```