Probability machines: consistent probability estimation using nonparametric learning machines.

ABSTRACT:

Background

Most machine learning approaches only provide a classification for binary responses. However, probabilities are required for risk estimation using individual patient characteristics. It has been shown recently that every statistical learning machine known to be consistent for a nonparametric regression problem is a probability machine that is provably consistent for this estimation problem.

Objectives

The aim of this paper is to show how random forests and nearest neighbors can be used for consistent estimation of individual probabilities.

Methods

Two random forest algorithms and two nearest neighbor algorithms are described in detail for estimation of individual probabilities. We discuss the consistency of random forests, nearest neighbors and other learni

SUBMITTER: Malley JD 

PROVIDER: S-EPMC3250568 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature