XGBoost is a powerful tool for multivariate regression tasks, where the goal is to predict a continuous target variable based on multiple input features.

This example demonstrates how to fit an XGBoost model for multivariate regression using the scikit-learn API in just a few lines of code.

```
# xgboosting.com
# Fit an XGBoost Model for Multivariate Regression using scikit-learn API
from sklearn.datasets import make_regression
from xgboost import XGBRegressor
# Generate a synthetic dataset with 5 features
X, y = make_regression(n_samples=1000, n_features=5, noise=0.1, random_state=42)
# Initialize XGBRegressor
model = XGBRegressor(objective='reg:squarederror', random_state=42)
# Fit the model to training data
model.fit(X, y)
# Make predictions with the fit model
predictions = model.predict(X)
print(predictions[:5])
```

The key steps:

- Initialize an
`XGBRegressor`

with the appropriate`objective`

(here,`'reg:squarederror'`

for regression with squared error loss). - Fit the model to your training data using
`fit()`

. - Make predictions on new data using
`predict()`

.

That’s it! You now have a working XGBoost model for multivariate regression. Experiment with different hyperparameters to fine-tune performance for your specific dataset and requirements.