Crit\`eres de qualit\'e d'un classifieur g\'en\'eraliste

TL;DR

This paper evaluates various classifiers based on criteria for generalist performance, concluding that random forests are the most suitable all-purpose classifier due to their balanced qualities.

Contribution

It establishes a set of criteria for assessing generalist classifiers and provides a comparative analysis of six popular classifiers.

Findings

Random forests are identified as the best generalist classifiers.

The paper provides a scoring system for classifier evaluation.

Tables summarize the relative performance of classifiers.

Abstract

This paper considers the problem of choosing a good classifier. For each problem there exist an optimal classifier, but none are optimal, regarding the error rate, in all cases. Because there exists a large number of classifiers, a user would rather prefer an all-purpose classifier that is easy to adjust, in the hope that it will do almost as good as the optimal. In this paper we establish a list of criteria that a good generalist classifier should satisfy . We first discuss data analytic, these criteria are presented. Six among the most popular classifiers are selected and scored according to these criteria. Tables allow to easily appreciate the relative values of each. In the end, random forests turn out to be the best classifiers.