What is Bias variance trade off?Bias variance trade off kya hai?

🧸 Think of it likeAise samjho

Like studying for an exam: only reading the summary misses things (too simple), but mugging up last year's paper word by word fails on new questions (too complex). The sweet spot is understanding the ideas.Exam ki padhai jaisa: sirf summary padhne se cheezein chhoot jaati hain (bahut simple), par pichhle saal ka paper ratne se naye sawaal pe fail (bahut complex). Sahi jagah hai concepts samajhna.

📘 ExplainedVistaar se

High bias means underfitting: the model is too simple, so it's wrong on training and new data alike. High variance means overfitting: it fits training data closely but fails on new data. Increasing complexity lowers bias but raises variance. You find the sweet spot using validation data, regularisation and more data.High bias matlab underfitting: model bahut simple hai, training aur naye dono data pe galat. High variance matlab overfitting: training data pe kareeb fit, par naye data pe fail. Complexity badhaane se bias ghatta aur variance badhta hai. Sweet spot validation data, regularisation aur zyaada data se milta hai.

💡 Real exampleAsli example

A straight line through curved sales data has high bias; a 50 level decision tree has high variance.Curved sales data pe seedhi line high bias hai; 50 level ka decision tree high variance.

📖 Learn it step by stepStep by step seekho

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

🔗 Related termsJude hue shabd

• OverfittingWhen a model memorises its training data, including noise, and does badly on new data.Jab model training data ko, noise samet, ratt leta hai aur naye data pe kharab karta hai.
• 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.