Overfitting (model menghafal data latihan dan gagal pada data baharu) dan underfitting (model terlalu mudah untuk menangkap corak) ialah dua masalah fundamental dalam ML. Mengimbanginya — mencapai generalisasi yang baik — adalah pusat kepada membina model yang berkesan.
Overfitting vs underfitting
OVERFITTING → the model learns the training data TOO well (including noise) →
→ performs great on training data but POORLY on new/unseen data (doesn't generalize)
→ too complex; memorizes rather than learns general patterns
→ like memorizing answers vs understanding the concept
UNDERFITTING → the model is TOO SIMPLE to capture the underlying patterns →
→ performs poorly on BOTH training and new data
→ not enough complexity/capacity to learn the patterns
→ the goal is GENERALIZATION: learn real patterns → perform well on NEW data
