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What is Overfitting & Underfitting

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Answer

Overfitting happens when a model learns the training data too closely, including its noise, and performs poorly on new data. Underfitting happens when a model is too simple to capture the real patterns in the data.
Real-world example A student who memorizes exact exam questions without understanding the topic is like an overfit model, while a student who barely studies at all is like an underfit model.

Common follow-ups: What is regularization, What is the bias variance tradeoff, What is cross validation

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All 7 Overfitting & Underfitting questions