15. Performance

Performance evaluation & improvement ## Episode Performance evaluation - Performance measures: accuracy, precision, recall, F1/F2 score
- Cross validation: split your data into train, validation, test sets
- Training set is for training your algorithm
- Validation set is to test your algorithm's performance. It can be used to inform changing your model (ie, hyperparameters)
- Test set is used for your final score. It can't be used to inform changing your model. Performance improvement

- Modify hyperpamaraters
- Data: collect more, fill in missing cells, normalize fields
- Regularize: reduce overfitting (high variance) and underfitting (high bias)

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