Landmark 2 stops to get here · leads to 2

Validation Set

A portion of data held out from training, used to tune hyperparameters and monitor overfitting.

Your route here

2 stops · basics first
  1. Dataset ✓ understood

    A collection of data examples used for training, validating, or testing machine learning models.

  2. Train-Test Split ✓ understood

    Dividing a dataset into separate portions for training the model and evaluating its performance on unseen data.

  3. Validation Set · you are here ✓ understood

Picture it

training stepslossbest checkpointtrainingvalidationoverfitting →
Notice that training loss keeps falling while validation loss turns upward; the validation minimum marks the checkpoint to keep.

Where it sits

Before this

Train-Test Split
Validation Set

Leads to

Early Stopping Hyperparameter Tuning

Explore nearby

Evaluation Test Set A final portion of data unseen during training and validation, used for unbiased evaluation of model performance. Evaluation Cross-Validation A technique for assessing model performance by partitioning data into subsets, training on some and validating on others. Training Early Stopping Stopping training when validation performance stops improving, preventing overfitting. Training Hyperparameter Tuning The process of finding optimal hyperparameter values through techniques like grid search, random search, or Bayesian optimization. Training Overfitting When a model fits its training data too closely, noise included, so it scores well on examples it has seen and poorly on new ones.