Early Stopping Check

Problem Statement

Determine if training should stop based on validation loss history.

Background

Early stopping is a regularization technique that stops training when the model stops improving on a validation set. This prevents overfitting to the training data.

The rule: stop if validation loss hasn't improved for patience consecutive epochs.

Your Task

Write a function should_stop(history_losses, patience) that returns True if training should stop.

Output Format

Return a boolean: True if should stop, False otherwise.