Webinar: Scoring Metrics for Classification Models

You have trained a classification model with a highly sophisticated Machine Learning algorithm. Right. It is now time to evaluate its performance on test data, i.e. to score it.

A number of scoring metrics have been proposed over the years in different domains: sensitivity and specificity, precision and recall, accuracy, area under the curve, Cohen’s Kappa, and many more. Generally, they are based on values reported in a confusion matrix.

[Bokaseca]

I just fall in love. Thanks for the explanation

[MaikaratuAcademicConsult]

Thank you for this easy-to-follow explanation.

[mshparber]

How can I easily compare several models using recall and precision scores? Thank you

[mattmatt]

What about unequal misclassification costs ? Can you score the model using cost matrix and see cost curves in the ROC space ?