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Statistics for Machine Learning

You're reading from Statistics for Machine Learning

Product type Book

Published in Jul 2017

Publisher Packt

ISBN-13 9781788295758

Pages 442 pages

Edition 1st Edition

Languages

Concepts

Author (1):

Pratap Dangeti

Table of Contents (16) Chapters

Title Page

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

Journey from Statistics to Machine Learning

Parallelism of Statistics and Machine Learning

Logistic Regression Versus Random Forest

Tree-Based Machine Learning Models

K-Nearest Neighbors and Naive Bayes

Support Vector Machines and Neural Networks

Recommendation Engines

Unsupervised Learning

Reinforcement Learning

Logistic regression – introduction and advantages

Logistic regression applies maximum likelihood estimation after transforming the dependent variable into a logit variable (natural log of the odds of the dependent variable occurring or not) with respect to independent variables. In this way, logistic regression estimates the probability of a certain event occurring. In the following equation, log of odds changes linearly as a function of explanatory variables:

One can simply ask, why odds, log(odds) and not probability? In fact, this is interviewers favorite question in analytics interviews.

The reason is as follows:

By converting probability to log(odds), we have expanded the range from [0, 1] to [- ∞, +∞ ]. By fitting model on probability we will encounter a restricted range problem, and also by applying log transformation, we cover-up the non-linearity involved and we can just fit with a linear combination of variables.

One more question one ask is what will happen if someone fit the linear...

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Authors (1)

Pratap Dangeti

Pratap Dangeti develops machine learning and deep learning solutions for structured, image, and text data at TCS, analytics and insights, innovation lab in Bangalore. He has acquired a lot of experience in both analytics and data science. He received his master's degree from IIT Bombay in its industrial engineering and operations research program. He is an artificial intelligence enthusiast. When not working, he likes to read about next-gen technologies and innovative methodologies.

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