The _______ paradox refers to a situation where a model’s performance on the training data improves while its performance on unseen data deteriorates.
- Bias
- Curse of Dimensionality
- Data Augmentation
- Overfitting
The Bias-Variance trade-off paradox is the situation where a model performs exceptionally well on its training data (low bias) but poorly on unseen data (high variance). This is typically caused by overfitting.
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