python随机森林分类模型

以下是Python中使用随机森林进行分类模型的代码示例：

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

# 创建随机森林分类器
forest = RandomForestClassifier()

# 将数据集分为训练集和测试集
x_train, x_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=42)

# 在训练集上拟合模型
forest.fit(x_train, y_train)

# 模型评估
score = forest.score(x_test, y_test)
print(score)  # 这里的score代表精确率（accuracy）

# 模型预测
predictions = forest.predict(x_test)
print(predictions)  # 输出预测结果

# 计算模型运行的时间
import time
start = time.time()
predictions = forest.predict(x_test)
end = time.time()
print("模型运行时间：", end - start)

随机森林是一个包含多个决策树的分类器，其输出的类别由各个树输出的类别的众数决定。你可以使用Scikit-learn库提供的RandomizedSearchCV类进行随机搜索，以找到最佳的超参数组合。以下是一个例子：

from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import RandomizedSearchCV

# 定义参数网格
param_grid = {
    "n_estimators": np.arange(100, 2000, step=100),
    "max_features": ["auto", "sqrt", "log2"],
    "max_depth": list(np.arange(10, 100, step=10)) + [None],
    "min_samples_split": np.arange(2, 10, step=2),
    "min_samples_leaf": [1, 2, 4],
    "bootstrap": [True, False]
}

# 创建随机森林回归器
forest = RandomForestRegressor()

# 进行随机参数调优
random_cv = RandomizedSearchCV(forest, param_grid, n_iter=100, cv=3, scoring="r2", n_jobs=-1)
random_cv.fit(X, y)

# 输出最佳参数
print("Best params:\n")
print(random_cv.best_params_)

希望这些代码对你有所帮助！<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
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