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Query-by-Committee: Vote-Entropy Active Learning (2D)

Interactive 2D simulator of Query-by-Committee active learning: a committee of logistic-regression classifiers votes on unlabeled points, vote entropy picks the next label to request, and an accuracy-vs-labels chart compares it live against a random-query baseline.

AI & Machine Learning2DAdvanced60 FPS📱 Mobile-adapted⇄ 3D version

Active learning tries to reach high accuracy from as few labeled examples as possible by choosing, at every step, which unlabeled point is worth asking a human to label. This simulator implements Query-by-Committee (QBC): a committee of logistic-regression classifiers is trained on bootstrap resamples of the labeled set, every unlabeled pool point gets a vote from each member, and the point with the highest vote entropy — the one the committee disagrees about most — is queried next. A random-query baseline runs alongside it from the same starting labels, and a live accuracy-vs-labels chart shows exactly how much the disagreement-driven strategy saves.

⚙ Under the hood

Watch a committee of logistic-regression classifiers vote on unlabeled points and pick the next label to request by vote entropy — the classic Query-by-Committee active-learning strategy, with a random-query baseline run side by side and a live accuracy-vs-labels chart.

active learningquery by committeemachine learninglogistic regressionvote entropydata science

2D · HTML5 Canvas 2D · 60 FPS target · runs fully client-side, no install