Explain the concept of k-fold Cross-Validation. What does "k" signify?
- Number of equally-sized folds the data is divided into
- Number of features in the dataset
- Number of iterations in training
- Number of layers in a deep learning model
In k-fold Cross-Validation, "k" signifies the number of equally-sized folds the data is divided into. The model is trained on (k-1) folds and validated on the remaining fold, repeating this process k times. The average performance across all k trials provides a more unbiased estimate of the model's capability.
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