AdaBoost: Sample Reweighting in Action

AdaBoost builds a strong classifier out of many weak ones by training them one at a time and reweighting the training set after each round: samples the current committee gets wrong get heavier, forcing the next weak learner to focus on exactly what the ensemble is still missing. This simulator renders that process in 3D on an XOR-like dataset (not solvable by any single axis-aligned cut) — sphere size tracks each sample's live weight, a highlighted stump appears every round with its computed weighted error ε and learner weight α, and the floor shades in the combined weighted-vote decision region as rounds accumulate, so you can watch a strong boundary emerge from a stack of individually weak ones.