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. 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. https://www.knime.com/knime-software/knime-analytics-platform View the slides here: https://www.slideshare.net/KNIMESlides/scoring-metrics-for-classification-models