Basics of feature selection and statistical learning for high-energy physics

This document introduces basics in data preparation, feature selection and learning basics for high-energy physics tasks. The emphasis is on feature selection by principal component analysis, information gain and significance measures for features. As examples for basic statistical learning algorithms, the maximum a posteriori and maximum likelihood classifiers are shown. Furthermore, a simple rule-based classification as a means for automated cut finding is introduced. Finally two toolboxes for the application of statistical learning techniques are introduced.

Duke Scholars

Author Anselm G. Vossen Physics