You've built a model with high variance. How can Cross-Validation help in diagnosing and improving the model?
- By automatically reducing the complexity of the model
- By helping in feature selection
- By providing a robust estimation of model performance and aiding hyperparameter tuning
- By providing more data for training
Cross-Validation provides a robust estimation of the model's performance across different data splits. For a high variance model, it can help in diagnosing the issue by highlighting overfitting and assist in hyperparameter tuning to find the best complexity that captures underlying patterns without fitting noise.
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