python随机森林进行回归预测代码

您可以分享以下 Python 随机森林进行回归预测的代码：

首先，需要导入必要的库：

from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
import pandas as pd
import numpy as np

接着，读取数据：

df = pd.read_csv('data.csv')

将数据集分为训练集和测试集：

X = df.drop('target_variable', axis=1)
y = df['target_variable']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

定义模型并进行训练：

rf = RandomForestRegressor(n_estimators=10, random_state=42)
rf.fit(X_train, y_train)

用训练好的模型进行预测：

y_pred = rf.predict(X_test)

计算模型预测的均方误差：

mse = mean_squared_error(y_test, y_pred)

完整的代码示例如下：

from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
import pandas as pd
import numpy as np

# 读取数据
df = pd.read_csv('data.csv')

# 将数据集分为训练集和测试集
X = df.drop('target_variable', axis=1)
y = df['target_variable']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 定义模型并进行训练
rf = RandomForestRegressor(n_estimators=10, random_state=42)
rf.fit(X_train, y_train)

# 用训练好的模型进行预测
y_pred = rf.predict(X_test)

# 计算模型预测的均方误差
mse = mean_squared_error(y_test, y_pred)