Input
ExecutionExecution trigger
AI/ML/Dataset
Generate K train/test splits for cross-validation. Each fold uses (K-1)/K data for training and 1/K for validation, and runs the connected fold branch once per fold.
Scores range from 0 to 10. Higher values mean more impact, exposure, or operational weight.
Execution trigger
Number of folds for cross-validation (typically 5 or 10)
Randomly shuffle data before splitting
Source database containing the dataset. It is only read, never modified.
Database to receive training data for each fold (will be cleared and filled K times)
Database to receive validation data for each fold (will be cleared and filled K times)
Executed K times, once per fold, and awaited before the next fold overwrites the databases. Connect your training/evaluation logic here.
Triggered after all folds completed successfully
Current fold index (0 to K-1)
Information about the K-fold split
Number of folds generated
Total number of samples in dataset
Approximate samples per fold