From Evaluation to Enlightenment: Delving into Out-of-Sample Predictions in Cross-Validation

Uncovering Insights and Overcoming Limitations through Out-of-Fold Predictions.

Understanding cross-validation and applying it in practical daily work is a must-have skill for every data scientist. While the primary purpose of cross-validation is to assess model performance and fine-tune hyperparameters, it offers additional outputs that should be noticed. By obtaining and combining predictions for each fold, we can…