Model Performance Calculator
Complete ML model evaluation. Classification metrics, regression analysis, cross-validation, ROC/AUC curves, and model comparison.
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How to Use This Calculator
- Enter actual vs. predicted values for your test set.
- Select the task type (classification or regression) to compute relevant metrics.
- Review classification metrics (accuracy, F1, AUC) or regression metrics (RMSE, MAE, R²).
- Check for systematic bias by inspecting residuals or class-level performance.
- Use the per-class metrics breakdown to identify which classes the model underperforms on.
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Formula
F1 = 2×(P×R)/(P+R) | R² = 1 - SS_res/SS_tot | AUC = ∫TPR d(FPR)Related Calculators
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