Skip to Content
Hands-On Automated Machine Learning
book

Hands-On Automated Machine Learning

by Sibanjan Das, Umit Mert Cakmak
April 2018
Beginner to intermediate content levelBeginner to intermediate
282 pages
6h 52m
English
Packt Publishing
Content preview from Hands-On Automated Machine Learning

Assumptions of OLS

All of these assumptions about the data should hold true to reap the benefits of the OLS regression techniques:

  • Linearity: The true underlying relationship between X and Y is linear.
  • Homoscedastic: The variance of residuals must be constant. The residual is the difference between the observed value and predictive value of the target.
  • Normality: The residuals/errors should be normally distributed.
  • No or little multicollinearity: The residuals/errors must be independent.

OLS is also affected by the presence of outliers in the data. Outlier treatment is necessary before one proceeds with linear regression modeling using OLS linear regression.

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Start your free trial

You might also like

Automated Machine Learning

Automated Machine Learning

Adnan Masood
R: Unleash Machine Learning Techniques

R: Unleash Machine Learning Techniques

Raghav Bali, Dipanjan Sarkar, Brett Lantz, Cory Lesmeister

Publisher Resources

ISBN: 9781788629898Supplemental Content