What Early Stopping Meaning, Applications & Example
Technique to prevent overfitting by halting training when validation metrics worsen.
What is Early Stopping?
Early Stopping is a regularization technique used in machine learning to prevent overfitting by halting training when a model ’s performance on a validation set starts to degrade. This helps in balancing between underfitting and overfitting, allowing the model to generalize better on new data.
How Early Stopping Works
During training, the model’s performance is monitored on a validation set. If the model stops improving on this set for a predefined number of epochs (patience), training is terminated. The model’s weights are then restored to the point where validation performance was optimal.
Applications of Early Stopping
- Neural Networks: Reduces overfitting in deep networks by stopping training at the optimal performance.
- Gradient Boosting: Controls overfitting in iterative algorithms like XGBoost by stopping tree additions early.
- Time Series Forecasting: Helps in models where long training can lead to poor generalization.
Example of Early Stopping in Use
In Keras, Early Stopping can be implemented as follows:
from tensorflow.keras.callbacks import EarlyStopping
early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)
model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=100, callbacks=[early_stopping])
Here, training will stop if validation loss doesn’t improve for 5 epochs, and the model’s best weights will be restored, leading to a model that generalizes well without overfitting.