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Overfitting occurs when a model memorizes the training data too precisely, including its noise and randomness, instead of learning the general underlying pattern. Imagine fitting curves to data points that roughly follow a parabolic shape. A linear model is too basic. Since it can’t capture the curve, it underfits, resulting in high error on both training and test data. A quadratic model reflects the true structure of the data, producing low training and test error - this is the right balance. B
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