使用python中的随机森林进行数据回归预测

**使用Python中的随机森林进行数据回归预测**

在机器学习领域，回归预测是指预测连续值的任务。随机森林是一种强大的机器学习算法，可以用于回归预测。在本文中，我们将使用Python中的scikit-learn库来实现一个简单的回归预测模型。

**数据准备**

首先，我们需要准备我们的数据集。假设我们有一个包含两列数据的CSV文件，分别是特征和目标值。

import pandas as pd#读取CSV文件df = pd.read_csv('data.csv')

# 查看数据集print(df.head())

# 将数据集分为训练集和测试集from sklearn.model_selection import train_test_splitX_train, X_test, y_train, y_test = train_test_split(df.drop('target', axis=1), df['target'], test_size=0.2, random_state=42)

from sklearn.preprocessing import StandardScaler# 创建StandardScaler对象scaler = StandardScaler()

# 对训练集和测试集进行标准化X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

from sklearn.ensemble import RandomForestRegressor# 创建RandomForestRegressor对象rf = RandomForestRegressor(n_estimators=100, random_state=42)

# 对训练集进行拟合rf.fit(X_train_scaled, y_train)

from sklearn.metrics import mean_absolute_error, mean_squared_error# 对测试集进行预测y_pred = rf.predict(X_test_scaled)

# 计算MAE和RMSEmae = mean_absolute_error(y_test, y_pred)
rmse = mean_squared_error(y_test, y_pred)**0.5print(f"MAE: {mae:.2f}")
print(f"RMSE: {rmse:.2f}")

from sklearn.model_selection import GridSearchCV# 定义参数空间param_grid = {
 'n_estimators': [10,50,100,200],
 'max_depth': [None,5,10]
}

# 创建GridSearchCV对象grid_search = GridSearchCV(estimator=rf, param_grid=param_grid, cv=5)

# 对训练集进行拟合grid_search.fit(X_train_scaled, y_train)

print(f"最佳参数组合: {grid_search.best_params_}")

import joblib#保存模型joblib.dump(rf, 'model.joblib')