Early stopping is a technique employed during the training of machine learning models to prevent overfitting. It involves monitoring the model's performance on a separate validation set during the training process and halting the training when the performance on the validation set starts to deteriorate.
During training, the model's performance on the training data generally improves, but at some point, it may begin to overfit—meaning it becomes too specialized to the training data and performs poorly on new, unseen data. Early stopping addresses this issue by stopping the training process before overfitting occurs.
By regularly evaluating the model's performance on the validation set, one can identify the point at which the validation performance stops improving and starts to worsen. This signals that the model has reached the optimal point for generalization, striking a balance between complexity and performance.
Early stopping helps prevent unnecessary computational resources from being expended on training an overfitted model. It allows the model to generalize better and improve its performance on unseen data, leading to more robust and reliable predictions.
Code Example:
callback = tf.keras.callbacks.EarlyStopping(
monitor="val_loss",
min_delta=0,
patience=0,
verbose=1,
mode="auto",
baseline=None,
restore_best_weights=False
)
history = model.fit(X_train,Y_train,validation_data=(X_test,Y_test),epochs=3500,callback=callback)