python如何实现决策树回归
时间: 2023-07-18 15:25:40 浏览: 38
决策树回归是一种基于决策树的回归方法,它通过构建决策树模型来预测连续型的输出变量。下面是Python实现决策树回归的基本步骤:
1. 导入所需的库和模块
```python
from sklearn.tree import DecisionTreeRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
import pandas as pd
import numpy as np
```
2. 加载数据集
```python
data = pd.read_csv('data.csv')
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values
```
3. 将数据集分为训练集和测试集
```python
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
```
4. 创建并训练决策树模型
```python
regressor = DecisionTreeRegressor(random_state=0)
regressor.fit(X_train, y_train)
```
5. 预测测试集的结果并计算均方误差
```python
y_pred = regressor.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print('均方误差:', mse)
```
以上就是使用Python实现决策树回归的基本步骤,可以根据具体的数据集和需求进行修改和调整。