Bias–variance tradeoff
id:
bias-variance-tradeoff-173-9281787
title:
Bias–variance tradeoff
text:
In statistics and machine learning, the bias–variance tradeoff describes the relationship between a model's complexity, the accuracy of its predictions, and how well it can make predictions on previously unseen data that were not used to train the model. In general, as we increase the number of tunable parameters in a model, it becomes more flexible, and can better fit a training data set. It is said to have lower error, or bias. However, for more flexible models, there will tend to be greater v
brand slug:
wiki
category slug:
encyclopedia
description:
Property of a model
original url:
https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff
date created:
2013-10-01T13:44:23Z
date modified:
2024-09-02T10:52:54Z
main entity:
{"identifier":"Q17003119","url":"https://www.wikidata.org/entity/Q17003119"}
image:
{"content_url":"https://upload.wikimedia.org/wikipedia/commons/6/64/Test_function_and_noisy_data.png","width":1201,"height":901}
fields total:
13
integrity:
16