Evaluation✓ Mathematical
◆ The PatternReliable model evaluation by rotating which data is used for testing
k-fold cross-validation averages performance over k splits for a more reliable estimate than a single train/test split.
CV Score = (1/k) · Σᵢ score(model_i, fold_i)
Train k models, each evaluated on a different held-out fold
// k-fold cross-validation — click to cycle through folds
k folds5
1
Split data into k folds
Typically k=5 or k=10
2
Train on k−1 folds
One fold held out as validation set
3
Evaluate on held-out fold
Record metric (accuracy, F1, etc.)
4
Average k scores
→ final CV estimate with confidence interval
Pattern bridge: Rotating train/test splits prevents overfitting to one sample — the statistical version of sampling distributions. In markets, recency bias is what happens when you only test on the latest fold.