In a situation where the training accuracy is high but the testing accuracy is low, what could be the issue, and how might you solve it?
- Model is overfitting
- Model is underfitting
- Testing data is too large
- Training data is too small
Overfitting occurs when a model performs well on the training data but poorly on unseen data. This could be a result of high complexity in the model. Solutions can include using cross-validation, adding regularizations, or simplifying the model by removing unnecessary features or reducing the complexity of the model itself.
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