Logistic Regression Inside and Out
Peer Reviewed
Field of study
Physical Sciences and Mathematics
Statistics and Probability
Applied Statistics
Logistic Regression Inside and Out
Logistic Regression Inside and Out with R Studio & SAS Studio
Jeffrey Strickland
Field of study
Physical Sciences and Mathematics
Statistics and Probability
Applied Statistics
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I am often asked if logistic regression is a machine learning algorithm. I say that it is not, for I can formulate it mathematically and solve it using matrix equations, for example. Its solution is derived deterministically and estimation is performed mathematically, through optimization methods. Its link function is a is a mathematical equation.

Logistic regression is most appropriate when the dependent variable (target variable) has two possible outcomes (binary). Will customers respond to an offer or unsubscribe, will the enemy fight or flee, will subjects respond to treatment or grow ill, will livestock live or die? Yes or no? One or zero?

Here I will take you on a journey into the art and science of predictive modeling using logistic regression, inside-and-out.

ISBN 978-1-5342-0240-5
Imprint Glasstree Academic Publishing
DOI 10.20850/9781534202405
Copyright 2017, Jeffrey Strickland
Copyright License Standard Copyright License
Product Details 6 x 9 Standard Mono Matte Perfect Bound
Page Count 334 pages
Type of Publication Textbook
Peer Review Status Open, Completed
Keywords logit, logistic regression, modeling, binary response, R Programming, R Studio, SAS Studio, Python, analytics, data analytics
Audience University/Post-secondary education
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