Accuracy assessment of applied supervised machine learning models on usual data probability distributions

Abstract

Abstract In this paper, an application analysis of supervised classification techniques on several probability distributions is carried out. Accuracy as well as usual standard metrics have been highlighted to rate the performance of generated learning models. Using data that fit different distributions, we investigated whether the application of a classification method had an optimizing impact on the accurateness of its correlated learning model.