Overfitting (model training data को memorize कर लेता है और new data पर विफल हो जाता है) और underfitting (model patterns को capture करने के लिए बहुत सरल है) ML में दो मौलिक समस्याएं हैं। इन्हें संतुलित करना — अच्छा generalization प्राप्त करना — प्रभावी models बनाने के केंद्र में है।
Overfitting vs underfitting
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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