- Loss on training data
- large:
- model bias -> add features
- optimization -> change optimization methods
- small:
- loss on testing data
- large:
- overfitting:
- (1) more training data, data augmentation
- (2) make model simpler
- overfitting:
- small:
- mismatch
- large:
- loss on testing data
- large:
[ML] General guide on ML
Rain Hu
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