ML Model Performance Visualizer
Data is randomly generated to demonstrate various regression and classification metrics. Try dragging the points!
Regression Performance (Continuous Predictions)
MAE
MSE
C-index
Classification Performance (Probabilities)
Brier Score
MCE
AUC
Regression Metrics
- Mean Absolute Error (MAE): average lengths of the residual segments
- Mean Squared Error (MSE): average area of the squares formed by the residuals
- Concordance Index (C-index): the agreement in the ordering of predicted and actual values
Classification Metrics (i.e., for binary outcomes 0/1)
- Brier Score: average squared difference between predicted probabilities and actual outcomes
- Mean Calibration Error (MCE): the average squared difference between predicted probabilities and observed frequencies across bins
- Area Under the ROC Curve (AUC): the agreement in the ordering of predicted probabilities for positive and negative classes