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Statistics for Machine Learning

You're reading from Statistics for Machine Learning Techniques for exploring supervised, unsupervised, and reinforcement learning models with Python and R

Product type Paperback

Published in Jul 2017

Publisher Packt

ISBN-13 9781788295758

Length 442 pages

Edition 1st Edition

Languages

Python

Concepts

Machine Learning

Author (1):

Pratap Dangeti

View More author details

Table of Contents (10) Chapters

Preface

1. Journey from Statistics to Machine Learning

2. Parallelism of Statistics and Machine Learning FREE CHAPTER

3. Logistic Regression Versus Random Forest

4. Tree-Based Machine Learning Models

5. K-Nearest Neighbors and Naive Bayes

6. Support Vector Machines and Neural Networks

7. Recommendation Engines

8. Unsupervised Learning

9. 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

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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