写xgboost回归预测模型代码

Sure, I can help you with that! Here's an example code for XGBoost regression model:

import pandas as pd
import numpy as np
import xgboost as xgb
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

# Load data
data = pd.read_csv('data.csv')

# Split data into train and test sets
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)

# Define XGBoost model
model = xgb.XGBRegressor(objective='reg:squarederror', n_estimators=1000, learning_rate=0.05)

# Train model
model.fit(X_train, y_train)

# Make predictions on test set
y_pred = model.predict(X_test)

# Evaluate model performance
mse = mean_squared_error(y_test, y_pred)
print('MSE: ', mse)

Hope this helps! Let me know if you have any further questions.