In machine learning, what does 'overfitting' refer to?
- The model is too simple to capture patterns in the data
- The model perfectly fits the training data but fails to generalize to new data
- The model performs poorly on both training and test data
- The model performs well on training data but poorly on new, unseen data
Overfitting occurs when a model fits the training data too closely, capturing noise and specificities that don't generalize well to new, unseen data. This can result in poor performance on test data.
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