Early Stopping Based on Validation Loss
Validation & Tuning DS practice problem on Onlearn.
Difficulty: easy.
Topics: Understanding Implement Early Stopping Based on Validation Loss, Patience Parameter, Minimum Delta Threshold, Validation Loss Tracking, Epoch-wise Convergence, Best Model Checkpointing, Model Evaluation & Selection, Optimization Theory, Supervised Learning, Software Engineering for ML, Statistical Inference, Hyperparameter Tuning, Iterative Training Procedures, Generalization Analysis, Loss Function Monitoring, Algorithm Control Flow.
Create a function to decide when to stop training a model early based on a list of validation losses. The early stopping criterion should stop training if the validation loss hasn't improved for a specified number of epochs (patience), and only count as improvement if the loss decreases by more than a certain threshold (min delta). Your function should return the epoch to stop at and the best epoch that achieved the lowest validation loss.