This folder contains 10 trained models obtained through 10-fold cross-validation. Each model corresponds to one fold from the training process, ensuring robust validation and generalization.
Model Count: 10
Model Type: XGBoost
These models can be loaded individually for prediction or combined (e.g., averaged) for ensemble inference on the test set.