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📊 Evaluation Metrics Hyperparameters

A 3D confusion-matrix scatter and live ROC / precision-recall curve that respond as you drag the decision threshold, class separation, class balance and F-beta weighting.

AI & Machine Learning3DAdvanced60 FPS

A 3D confusion-matrix point cloud and a live ROC / precision-recall curve show exactly how the decision threshold, class balance, class separation and F-β weighting reshape every headline evaluation metric.

🔬 What It Demonstrates

Every sample's score places it left or right of a movable threshold plane, colouring it as a true positive, false positive, true negative or false negative — and the ROC/PR curve traces how those counts trade off as the threshold sweeps end to end.

🎮 How to Use

Drag the threshold slider and watch accuracy, precision, recall, F1 and F-β update instantly. Change class separation and balance to regenerate the dataset, and switch the curve panel between ROC and Precision–Recall.

💡 Did You Know?

ROC AUC is threshold-independent, but it can look deceptively strong on imbalanced data — that's why practitioners often pair it with Precision-Recall AUC or a task-specific F-β when positives are rare.

⚙ Under the hood

A 3D confusion-matrix scatter and live ROC / precision-recall curve that respond as you drag the decision threshold, class separation, class balance and F-beta weighting.

machine learningdata analysisroc curveprecision recallconfusion matrixclassificationThree.js

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install