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You're reading from Statistics for Machine Learning

Product typeBook

Published inJul 2017

Reading LevelIntermediate

PublisherPackt

ISBN-139781788295758

Edition1st Edition

Languages

Python

Concepts

Statistics

Author (1)

Pratap Dangeti

Maximum likelihood estimation

Logistic regression works on the principle of maximum likelihood estimation; here, we will explain in detail what it is in principle so that we can cover some more fundamentals of logistic regression in the following sections. Maximum likelihood estimation is a method of estimating the parameters of a model given observations, by finding the parameter values that maximize the likelihood of making the observations, this means finding parameters that maximize the probability p of event 1 and (1-p) of non-event 0, as you know:

probability (event + non-event) = 1

Example: Sample (0, 1, 0, 0, 1, 0) is drawn from binomial distribution. What is the maximum likelihood estimate of μ?

Solution: Given the fact that for binomial distribution P(X=1) = μ and P(X=0) = 1- μ where μ is the parameter:

Here, log is applied to both sides of the equation for mathematical convenience; also, maximizing likelihood is the same as the maximizing log of likelihood:

Determining the maximum...

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Statistics for Machine Learning

Published in: Jul 2017Publisher: PacktISBN-13: 9781788295758

Author (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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Maximum likelihood estimation

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