What is early stopping in boosting and how to validate it correctly?

Early stopping is a method to prevent overfitting in boosting algorithms (e.g., XGBoost, LightGBM). The idea is to stop training when the model's performance on the validation set ceases to improve.

How it works:

• Data is split into training and validation sets.
• During training, after each iteration (adding a new base learner), the metric on the validation set is evaluated.
• If the metric does not improve for a specified number of iterations (early_stopping_rounds), training is halted.

Proper validation:

• The validation set should be independent of the training set.
• The metric should reflect the task (e.g., AUC for classification).
• Use cross-validation with early stopping if possible to obtain a more reliable estimate.

Example in LightGBM:

import lightgbm as lgb

train_data = lgb.Dataset(X_train, label=y_train)
valid_data = lgb.Dataset(X_valid, label=y_valid)

params = {'objective': 'binary', 'metric': 'auc'}

model = lgb.train(params, train_data, valid_sets=[valid_data], early_stopping_rounds=10)

Thus, the model will stop if the validation performance does not improve over 10 iterations.