Machine learning
Composed builders over the same layers, fed by pure metrics.
Evaluation
| Builder | Shows |
|---|---|
addConfusionMatrix | counts or row-normalised recall, with per-cell labels |
addRocCurve | ROC + chance diagonal, AUC in the legend |
addPrCurve | precision–recall + no-skill baseline, AP in the legend |
addCalibration | reliability diagram + ECE |
addLiftCurve | cumulative gain or lift against the fraction targeted |
addPredVsActual | predictions against truth with the y=x reference and R² |
addResiduals | residuals against the prediction or the sample order |
addLearningCurve | train/validation score vs. training-set size, with CV bands |
Explainability & training
addFeatureImportance · addShapBeeswarm · addPartialDependence (PDP + ICE) · addAttentionMap · addTrainingCurves (EMA smoothing + best-epoch marker) · addRidgeline (distributions over epochs) · addEmbedding · addDecisionBoundary
Pure metrics
Classification — confusionMatrix, rocCurve, prCurve, calibrationCurve, classificationReport (per-class precision/recall/f1 plus macro and weighted averages), rocCurveOvR (one-vs-rest with macro/micro AUC), liftCurve, logLoss, brierScore.
Regression — r2, mse, rmse, mae.
Reducers — pca(data, n, d, k), standardize, emaSmooth, beeswarmLayout.
A class the model never predicts scores 0 rather than NaN, so macro averages stay comparable across runs.