Summary -Cross Validation in Neural Network

  • Cross-validation is like rotating test groups in a competition to avoid bias.
  • It gives better performance estimates and helps detect overfitting.
  • Simple manual implementation can be done even without libraries like sklearn.
  • Look for small data, high variance, or tuning needs to decide when CV is essential.

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