What is Regularisation?Regularisation kya hai?

🧸 Think of it likeAise samjho

Like a word limit on an essay: it forces you to keep only what matters instead of stuffing in every detail.Essay ki word limit jaisa: aapko sirf zaroori cheezein rakhne pe majboor karta hai, har detail thoonsne ki jagah.

📘 ExplainedVistaar se

Common forms: penalising large weights (L2 or ridge, weight decay), pushing some weights to zero (L1 or lasso), dropout in neural networks, limiting tree depth, and early stopping. Its strength is a hyperparameter tuned on validation data.Common roop: bade weights pe penalty (L2 ya ridge, weight decay), kuch weights ko zero tak dhakelna (L1 ya lasso), neural networks mein dropout, tree depth seemit karna, aur early stopping. Iski taakat ek hyperparameter hai jo validation data pe tune hota hai.

💡 Real exampleAsli example

Adding weight decay to stop a model fitting random noise in a small dataset.Chhote dataset mein random noise fit hone se rokne ke liye weight decay jodna.

📖 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.
• DropoutRandomly switching off some neurons during training so the network doesn't rely on any single one.Training mein kuch neurons randomly band karna taaki network kisi ek pe nirbhar na rahe.
• Early stoppingStopping training when the validation score stops improving, to avoid overfitting.Validation score sudharna band ho to training rok dena, overfitting se bachne ke liye.