Model Validation Lab

Compare validation strategies—Hold-out, K-Fold, Repeated K-Fold, Nested CV, GroupKFold, and Time-Series (walk-forward). See split diagrams, per-fold metrics, pooled ROC/PR, and how leakage can inflate results.


Split Diagram
Train (blue) • Validation (purple) • Test (orange)
Warning: Preprocessing before splitting can leak information. Fit transformers inside each training fold.
Per-Fold Metrics
Choose metric on the right; see mean ± std across folds
Mean: — Std: — Runtime (sim): —
ROC & PR (pooled across validation folds)
Discrimination performance; PR is more informative for rare positives
ROC-AUC: — PR-AUC: — Log Loss: —
Validation Method
Data & Splits

How to interpret

Hold-out vs Cross-Validation: Hold-out is fast for large data; K-fold and Repeated K-fold give more stable estimates on smaller datasets.

Nested CV: Use an inner loop to tune hyperparameters and an outer loop to estimate unbiased performance of the tuned model.

Group & Time-Series: Keep groups intact (GroupKFold) and preserve time order (walk-forward) to avoid leakage.

Leakage: Fit preprocessing inside each training fold. If you flip the leakage toggle, watch metrics become unrealistically optimistic.

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About This Tool & Methodology

This lab demonstrates proper validation workflows: holdout splits, k‑fold cross‑validation, stratification under imbalance, and leakage checks. It summarizes metrics and variance across folds.

Learning Outcomes

  • Design reliable train/validation/test splits without leakage.
  • Use cross‑validation to estimate generalization and model selection.
  • Understand stratification and when it’s necessary.

Authorship & Review

  • Author: 8gwifi.org engineering team
  • Reviewed by: Anish Nath
  • Last updated: 2025-11-19

Trust & Privacy

  • Runs locally with synthetic or provided datasets.