Tutorial example on optimizing three-class classifiers with ROC analysis
Sometimes, one of the classes in multi-class problem is much larger than the remaining classes. Classifiers, trained in such imbalanced problem, usually deliver very poor performances with a default decision function. The reason is that the model output of the large class dominates the solution. The default procedure of making decisions assumes that all the classes are equally important which results in high misclassification of small classes.
PRSD Studio allows you to quickly optimize multi-class classifiers in imbalanced problems. Watch the video inside!
In this video example, we illustrate some of the interactive tools applicable for classifier tuning.
For a detailed step-by-step example, see the knowledge base article.
This video requires a more recent version of the Adobe Flash Player to display. Please update your version of the Adobe Flash Player.