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Getting Your Bias Variance Right and Regularization

by Divisha Bera
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 7
Year of Publication: 2018
Authors: Divisha Bera
10.5120/ijca2018917579

Divisha Bera . Getting Your Bias Variance Right and Regularization. International Journal of Computer Applications. 181, 7 ( Aug 2018), 18-20. DOI=10.5120/ijca2018917579

@article{ 10.5120/ijca2018917579,
author = { Divisha Bera },
title = { Getting Your Bias Variance Right and Regularization },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2018 },
volume = { 181 },
number = { 7 },
month = { Aug },
year = { 2018 },
issn = { 0975-8887 },
pages = { 18-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume181/number7/29784-2018917579/ },
doi = { 10.5120/ijca2018917579 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:05:18.121959+05:30
%A Divisha Bera
%T Getting Your Bias Variance Right and Regularization
%J International Journal of Computer Applications
%@ 0975-8887
%V 181
%N 7
%P 18-20
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

It is clear that with ever improving computational power and endless data, there have been more breakthroughs in Machine Learning. Some practices have clearly emerged as promising while building a neural network. A performance metric to judge the model, is to see if it is in the wrong side of bias or variance. While building a classifier, cases with high bias, and high variance crop up. This paper shall attempt to shed some light on the problem of bias-variance, and how to solve them, with some approaches to perform Regularization.

References
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  2. Bengio, Yoshua, and Yves Grandvalet. 2004. “No Unbiased Estimator of the Variance of K-Fold Cross-Validation.” J. Mach. Learn. Res. 5 (December). JMLR.org: 1089–1105.
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  10. Coursera 2018 deeplearning.ai
Index Terms

Computer Science
Information Sciences

Keywords

Machine Learning Bias Variance Neural Networks Regularization.

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