- Statistics for Machine Learning
- Pratap Dangeti
- 101字
- 2021-07-02 19:05:56
When to stop tuning machine learning models
When to stop tuning the hyperparameters in a machine learning model is a million-dollar question. This problem can be mostly solved by keeping tabs on training and testing errors. While increasing the complexity of a model, the following stages occur:
- Stage 1: Underfitting stage - high train and high test errors (or low train and low test accuracy)
- Stage 2: Good fit stage (ideal scenario) - low train and low test errors (or high train and high test accuracy)
- Stage 3: Overfitting stage - low train and high test errors (or high train and low test accuracy)

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