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.
In this webinar, we will explore the concept of confusion matrix, true/false positives/negatives, and the related, most commonly used scoring metrics for classification models. And finally, we will show how to calculate all those metrics within KNIME Analytics Platform.
Join us on May 7 at 6:00 PM CEST to find out more about scoring a classification model.