Overfitting is expected when we observe that?

Answer: Overfitting is observed when the training error decreases but the test error increases.

Why this indicates overfitting: The model is learning patterns that fit the training data (including noise or idiosyncrasies) rather than capturing underlying general patterns, so it performs worse on unseen data.

• Training behavior: training error decreases steadily.

• Validation/test behavior: validation or test error begins to increase while training error keeps falling.

• Interpretation: the model has high variance and poor generalization to new data.

How to detect: Monitor training and validation/test loss curves; overfitting is signaled when validation loss bottoms out and then increases while training loss continues to fall.

Remedies:

• Use early stopping to halt training when validation error starts increasing.

• Apply regularization techniques (L1/L2 weight penalties, dropout) to reduce model complexity.

• Simplify the model (fewer parameters) or gather more labeled data.

• Use data augmentation and cross-validation to improve generalization and robustly select hyperparameters.

Summary: Watch both training and validation/test errors. Overfitting is characterized by a decreasing training error with an increasing test error; take corrective measures listed above to improve generalization.