如何评估随机森林模型预测回归问题的性能,请给出相关代码
时间: 2024-05-06 19:20:32 浏览: 8
评估随机森林模型预测回归问题的性能通常使用以下指标:
1. 均方误差(Mean Squared Error,MSE)
2. 均方根误差(Root Mean Squared Error,RMSE)
3. 平均绝对误差(Mean Absolute Error,MAE)
4. 决定系数(Coefficient of Determination,R2)
以下是评估随机森林模型预测回归问题性能的代码示例:
```python
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
from sklearn.model_selection import train_test_split
import pandas as pd
# 读取数据
data = pd.read_csv('data.csv')
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(data.drop('target', axis=1), data['target'], test_size=0.2, random_state=42)
# 定义随机森林模型
rf = RandomForestRegressor(n_estimators=100, random_state=42)
# 拟合模型
rf.fit(X_train, y_train)
# 预测测试集
y_pred = rf.predict(X_test)
# 评估模型性能
mse = mean_squared_error(y_test, y_pred)
rmse = mean_squared_error(y_test, y_pred, squared=False)
mae = mean_absolute_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
# 输出评估结果
print("MSE: ", mse)
print("RMSE: ", rmse)
print("MAE: ", mae)
print("R2: ", r2)
```
其中,data.csv为回归数据集,包含多个特征和目标变量。将数据集划分为训练集和测试集,定义随机森林模型,并使用训练集拟合模型。使用测试集进行预测,并计算评估指标。最后输出评估结果。