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Classifier Threshold & ROC Curve Explorer (2D)

Interactive 2D supervised-learning simulator: drag a classification threshold across two overlapping score-distribution histograms, watch the confusion matrix update live, and trace a real ROC curve with numerically-integrated AUC.

AI & Machine Learning2DModerate60 FPS📱 Mobile-adapted⇄ 3D version

Every supervised classifier — logistic regression, SVM, a random forest, a neural net — reduces a sample to a single score, then a threshold turns that score into a "positive" or "negative" prediction. This 2D simulator plots two overlapping score-distribution histograms, lets you drag a real decision threshold through them while every point recolors live by confusion-matrix outcome (TP/FP/TN/FN), and plots the resulting ROC curve with a numerically-integrated AUC — the exact tradeoff behind precision, recall and F1 in the article above.

⚙ Under the hood

Drag a real decision threshold across two overlapping classifier score distributions in 2D and watch every sample recolor live by confusion-matrix outcome, while a second panel traces the resulting ROC curve with a numerically-integrated AUC.

machine-learningclassificationroc-curveconfusion-matrixsupervised-learningauc

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