When a model performs well on training data but poorly on unseen data, what issue might it be facing?
- Overfitting
- Underfitting
- Data leakage
- Bias-variance tradeoff
The model is likely facing the issue of overfitting. Overfitting occurs when the model learns the training data too well, including noise, resulting in excellent performance on the training set but poor generalization to unseen data. It's an example of a high-variance problem in the bias-variance tradeoff. To address overfitting, techniques like regularization and more data are often used.
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