What is Overfitting?Overfitting kya hai?

🧸 Think of it likeAise samjho

Like a student who mugs up last year's question paper: perfect on those questions, lost on new ones.Jaise student pichhle saal ka paper rat leta hai: unhi sawaalon pe perfect, naye sawaalon pe kho jaata hai.

📘 ExplainedVistaar se

Signs: very high training score, much lower validation score. Causes: a model too complex for the data, too little data, training too long. Fixes: more or cleaner data, simpler models, regularisation, dropout, early stopping and cross validation.Lakshan: training score bahut ooncha, validation score kaafi kam. Wajah: data ke hisaab se bahut complex model, kam data, bahut der training. Upaay: zyaada ya saaf data, simple models, regularisation, dropout, early stopping aur cross validation.

💡 Real exampleAsli example

98% training accuracy but 65% on the test set.98% training accuracy par test set pe 65%.

📖 Learn it step by stepStep by step seekho

• Train/test split and overfittingTrain/test split aur overfitting

🔗 Related termsJude hue shabd

• UnderfittingWhen a model is too simple to capture the real patterns, so it does badly even on training data.Jab model asli patterns pakadne ke liye bahut simple ho, aur training data pe bhi kharab kare.
• RegularisationTechniques that keep a model simpler so it generalises better and overfits less.Tarike jo model ko simple rakhte hain taaki woh behtar generalise kare aur kam overfit ho.
• Bias variance trade offThe balance between a model that's too simple (misses patterns) and too complex (memorises noise).Bahut simple (patterns chhoot jaate hain) aur bahut complex (noise ratt leta hai) model ke beech ka santulan.