XGBoost allows you to save a trained model to disk and load it later to resume training.

This is useful for iterative model development, as you can train a model incrementally, saving progress along the way.

Here’s how you can resume training an XGBoost model using the `xgb_model`

parameter in the `fit()`

method.

```
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from xgboost import XGBRegressor
# Generate synthetic regression dataset
X, y = make_regression(n_samples=1000, n_features=10, noise=0.1, random_state=42)
# Split data into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Initialize XGBRegressor and train for 50 rounds
model = XGBRegressor(n_estimators=50, random_state=42)
model.fit(X_train, y_train)
# Save model to disk
model.save_model("model.json")
# Load pre-trained model
model_loaded = XGBRegressor(random_state=42)
model_loaded.load_model("model.json")
# Resume training for 50 more rounds
model_loaded.fit(X_train, y_train, xgb_model=model_loaded.get_booster())
# Make predictions and evaluate performance
y_pred = model_loaded.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print(f"Mean Squared Error: {mse:.4f}")
```

In this example:

We generate a synthetic regression dataset using

`make_regression()`

.We initialize an

`XGBRegressor`

and train it for 50 rounds.We save the trained model to disk using

`save_model()`

.We create a new

`XGBRegressor`

instance and load the saved model using`load_model()`

.We resume training the loaded model for an additional 50 rounds by passing the loaded model to the

`xgb_model`

parameter in`fit()`

. We use`get_booster()`

to get the underlying`Booster`

object.Finally, we make predictions with the fully trained model and evaluate its performance using mean squared error.

By using the `xgb_model`

parameter, we can seamlessly continue training a model from where we left off, allowing for flexible and efficient model development workflows.