What is Accuracy?Accuracy kya hai?

🧸 Think of it likeAise samjho

Like your exam score: 45 out of 50 questions right means 90%. But if almost every answer is 'No', always writing 'No' also scores high, so accuracy alone can fool you.Jaise exam ka score: 50 mein se 45 sahi matlab 90%. Par agar lagbhag har jawaab 'Nahi' hai, toh hamesha 'Nahi' likhne se bhi score achha aayega, isliye sirf accuracy dhokha de sakti hai.

📘 ExplainedVistaar se

Accuracy = correct predictions ÷ all predictions. It's easy to understand but misleading when one class is rare: if 1% of payments are fraud, a model that always says 'not fraud' is 99% accurate and completely useless. For imbalanced problems, look at precision, recall and the confusion matrix, and always compare against a simple baseline.Accuracy = sahi predictions ÷ saari predictions. Samajhna aasaan hai par tab misleading hai jab ek class rare ho: agar 1% payments fraud hain, to hamesha 'fraud nahi' bolne wala model 99% accurate hai aur bilkul bekaar. Imbalanced problems mein precision, recall aur confusion matrix dekho, aur hamesha simple baseline se compare karo.

💡 Real exampleAsli example

A spam filter that's right on 950 of 1,000 emails has 95% accuracy.Jo spam filter 1,000 mein se 950 emails pe sahi hai, uski accuracy 95% hai.

📖 Learn it step by stepStep by step seekho

• Measuring models: accuracy, precision and recallModels ko measure karna: accuracy, precision aur recall

🔗 Related termsJude hue shabd

• PrecisionOf everything the model flagged as positive, the share that really was positive.Model ne jitne positive flag kiye, unme se sach mein positive ka hissa.
• RecallOf all the real positives, the share the model caught.Saare asli positives mein se model ne kitne pakde, uska hissa.
• Confusion matrixA 2×2 (or bigger) table showing correct and wrong predictions for each class.2×2 (ya badi) table jo har class ke sahi aur galat predictions dikhati hai.
• BaselineA simple reference result that any new model must beat to be worth using.Ek simple reference result jise naye model ko use ke layak hone ke liye harana hi chahiye.