Platt scaling
id:
platt-scaling-229-626491
title:
Platt scaling
text:
In machine learning, Platt scaling or Platt calibration is a way of transforming the outputs of a classification model into a probability distribution over classes. The method was invented by John Platt in the context of support vector machines,
replacing an earlier method by Vapnik,
but can be applied to other classification models.
Platt scaling works by fitting a logistic regression model to a classifier's scores.
brand slug:
wiki
category slug:
encyclopedia
description:
Machine learning calibration technique
original url:
https://en.wikipedia.org/wiki/Platt_scaling
date created:
2014-03-10T21:10:07Z
date modified:
2024-09-15T07:16:07Z
main entity:
{"identifier":"Q17146653","url":"https://www.wikidata.org/entity/Q17146653"}
image:
fields total:
13
integrity:
15