Model Comparison
5 models trained on the same data. Champion selected by lowest MAE on holdout set.
Performance Comparison
5 models predict BOC1 price levels. MAE measures prediction error. Directional accuracy measures true next-day direction calls. Click headers to sort.
Residual Diagnostics
Champion model: loading
Feature Importance
XGBoost native gain-based importance
Learning Curves
Training vs validation MAE as function of training set size
Walk-Forward Performance by Fold
Does the model break down in specific periods?
Model Selection & Hyperparameters
Why Ridge?
Ridge regression was selected as champion for lowest MAE (2.69 c/lb) on the holdout set. Despite XGBoost's ability to capture nonlinear interactions, Ridge's L2 regularization prevented overfitting on this relatively small feature set (5 commodity prices). XGBoost's MAE was 2.5x worse — it memorizes noise in the training data rather than learning generalizable patterns.
Selection criterion: lowest MAE on 20% temporal holdout (no shuffle). Walk-forward validation on 5 folds confirms generalization.