What is Data leakage?Data leakage kya hai?

🧸 Think of it likeAise samjho

Like a student who saw the answer key before the exam: the score looks amazing, but it says nothing about what they really know.Jaise student ne exam se pehle answer key dekh li: score shaandaar dikhta hai, par asli samajh ke baare mein kuch nahi batata.

📘 ExplainedVistaar se

Examples: using 'refund issued' to predict fraud, scaling with statistics from the whole dataset before splitting, or the same customer in train and test. The model looks brilliant in testing and fails in production. Ask for every feature: would I know this at the moment of prediction?Examples: fraud predict karne ke liye 'refund issued' use karna, split se pehle poore dataset ke statistics se scaling, ya same customer train aur test dono mein. Model testing mein shaandaar dikhta hai aur production mein fail. Har feature ke liye poochho: prediction ke moment pe kya yeh mujhe pata hoga?

💡 Real exampleAsli example

A churn model scoring 0.99 AUC because it used 'account closed date' as a feature.'account closed date' ko feature banane se 0.99 AUC dene wala churn model.

📖 Learn it step by stepStep by step seekho

• Train/test split and overfittingTrain/test split aur overfitting
• Feature engineering: turning raw data into useful inputsFeature engineering: raw data ko kaam ke inputs mein badalna

🔗 Related termsJude hue shabd

• FeatureOne input the model uses to make a prediction, like age, income or number of logins.Ek input jo model prediction ke liye use karta hai, jaise umar, income ya logins ki ginti.
• Test setData kept aside until the very end to get an honest measure of how a model performs on new data.Data jo bilkul aakhir tak alag rakha jaata hai taaki pata chale model naye data pe sach mein kaisa karta hai.
• 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.