Stacking Ensembles: Meta-Learner Blending (2D)

Stacked generalization combines several different models by training a second-level "meta-learner" on top of their predictions rather than simply averaging or voting. This 2D canvas simulator fits three genuinely different base learners on a held-out slice of a synthetic two-class dataset whose true boundary runs diagonally through both features — a least-squares linear regression that only ever looks at the x-axis, a kernel-weighted nearest-neighbor classifier that only ever looks at the y-axis, and a threshold-search decision stump that picks whichever single axis works best. Because each base learner is missing real information the others have, it collects their honest out-of-sample predictions on a second held-out slice and trains a logistic-regression meta-learner on those predictions. A live bar chart then measures and compares real test error for every base model individually, for simple unweighted averaging, and for the trained stacked ensemble — making visible why a trained combination of partial-information models reliably beats any one of them alone.