A comparison of machine learning methods for classification using simulation with multiple real data examples from mental health studies.

ABSTRACT:

Background

Recent literature on the comparison of machine learning methods has raised questions about the neutrality, unbiasedness and utility of many comparative studies. Reporting of results on favourable datasets and sampling error in the estimated performance measures based on single samples are thought to be the major sources of bias in such comparisons. Better performance in one or a few instances does not necessarily imply so on an average or on a population level and simulation studies may be a better alternative for objectively comparing the performances of machine learning algorithms.

Methods

We compare the classification performance of a number of important and widely used machine learning algorithms, namely the Random Forests (RF), Support Vector Machines (SVM),

SUBMITTER: Khondoker M 

PROVIDER: S-EPMC5081132 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature