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How to Use XGBoost EarlyStopping Callback

The EarlyStopping callback in XGBoost provides a simple way to stop training early if a specified performance metric stops improving on a validation set.

This helps avoid overfitting and reduces training time by terminating the training process once the model’s performance on unseen data stops getting better.

Implementing early stopping is straightforward - simply create an EarlyStopping callback object and pass it to the callbacks parameter of xgb.train().

Here’s a complete example demonstrating how to use the EarlyStopping callback:

from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn.metrics import root_mean_squared_error
import xgboost as xgb

# Load example dataset
data = fetch_california_housing()
X, y = data.data, data.target

# Split data into train and validation sets
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)

# Create DMatrix objects
dtrain = xgb.DMatrix(X_train, label=y_train)
dval = xgb.DMatrix(X_val, label=y_val)

# Define XGBoost parameters
params = {
    'objective': 'reg:squarederror',
    'max_depth': 3,
    'learning_rate': 0.1,
    'subsample': 0.8,
    'colsample_bytree': 0.8,

# Create EarlyStopping callback
early_stop = xgb.callback.EarlyStopping(

# Train model with early stopping
model = xgb.train(
    evals=[(dtrain, "train"), (dval, "validation_0")],

# Make predictions and evaluate performance
y_pred = model.predict(dval)
rmse = root_mean_squared_error(y_val, y_pred)
print(f"Final RMSE: {rmse:.3f}")

print(f"Best iteration: {model.best_iteration}")
print(f"Best score: {model.best_score}")

In this example:

  1. We load the Housing dataset and split it into train and validation sets.
  2. We create DMatrix objects for XGBoost and define the model parameters.
  3. We create an EarlyStopping callback object, specifying:
    • rounds: Number of rounds to wait for improvement before stopping (10 in this case)
    • metric_name: Metric to monitor for improvement (‘rmse’)
    • data_name: Name of the validation set (“validation_0”)
    • save_best: Whether to save the best model (True)
  4. We train the model with xgb.train(), passing the EarlyStopping callback.
  5. We make predictions on the validation set and evaluate the model’s performance.
  6. Finally, we print the best iteration and score, confirming that the model stopped training early based on the EarlyStopping criteria.

The EarlyStopping callback provides a convenient way to automate the process of finding the optimal number of boosting rounds, helping to prevent overfitting and reduce training time. By monitoring a specified metric on a validation set, it allows the model to stop training once it starts to overfit, ensuring the best performance on unseen data.

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