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
- Follow the step-by-step wizard — complete each step before moving to the next. This guided approach ensures you provide all the necessary information in a logical order.
- Evaluation Type — Calculation mode to use. Choose from: Classification Metrics, Regression Metrics, Cross-Validation, ROC/AUC Analysis, Model Comparison. default: 0.
- True Positives (TP) — enter a numeric value. default: 85.
- True Negatives (TN) — enter a numeric value. default: 90.
- False Positives (FP) — enter a numeric value. default: 10.
- False Negatives (FN) — enter a numeric value. default: 15.
- Mean of Actual Values — enter a numeric value. default: 100.
- Mean of Predictions — enter a numeric value. default: 98.
- Mean Squared Error — enter a numeric value. default: 25.
- Number of Samples — enter a numeric value. default: 100.
- Variance of Actual Values — enter a numeric value. default: 400.
- Number of Folds — enter a numeric value. default: 5.
- Mean CV Score — enter a numeric value. default: 0.85.
- Std Dev of Scores — enter a numeric value. default: 0.03.
- Training Set Size — enter a numeric value. default: 10,000.
- AUC Score — Area Under ROC Curve default: 0.85.
- Classification Threshold — enter a numeric value. default: 0.5.
- Positive Class Rate — Prevalence of positive class default: 0.3.
- Model 1 Accuracy — enter a numeric value. default: 0.85.
- Model 2 Accuracy — enter a numeric value. default: 0.82.
- Model 1 Parameters — enter a numeric value. default: 1,000,000.
- Model 2 Parameters — enter a numeric value. default: 100,000.
- Test Set Size — enter a numeric value. default: 1,000.
- Once all inputs are set, review your results in the Results panel. Here's what each output means:
- Accuracy — shown as a percentage. This is the primary result of this calculator.
- F1 Score — shown as a percentage. This is the primary result of this calculator.
- RMSE — shown as a numeric value. This is the primary result of this calculator.
- R² — shown as a numeric value. This is the primary result of this calculator.
- Mean Score — shown as a percentage. This is the primary result of this calculator.
- AUC — shown as a numeric value. This is the primary result of this calculator.
- Significant — your calculated result. This is the primary result of this calculator.
- Precision — shown as a percentage.
- Recall — shown as a percentage.
- MCC — shown as a numeric value.
- Model Quality — your calculated result.
- MAE — shown as a numeric value.
- MAPE (%) — shown as a percentage.
- Fit Quality — your calculated result.
- 95% CI Lower — shown as a percentage.
- 95% CI Upper — shown as a percentage.
- Stability — your calculated result.
- Interpretation — your calculated result.
- Gini Coefficient — shown as a numeric value.
- Optimal Threshold — shown as a numeric value.
- Accuracy Difference — shown as a percentage.
- Effect Size (h) — shown as a numeric value.
- Recommendation — your calculated result.
- View the Performance Metrics below for a visual breakdown of how the numbers relate to each other.
- Check the Model Evaluation for a detailed row-by-row breakdown. This is useful for spotting trends or finding values at specific points.
- Explore the related calculators below if you need deeper analysis or want to approach this topic from a different angle.
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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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