You reduced the complexity of your model to prevent overfitting, but it led to underfitting. How would you find a balance between complexity and fit?
- Add regularization
- All of the above
- Increase dataset size
- Try cross-validation
Finding a balance might involve using cross-validation to systematically find the right level of complexity that fits well with the training data but also generalizes well to the validation data. This process helps in finding the right hyperparameters without biasing the test data.
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