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Applied Supervised Learning with R

You're reading from  Applied Supervised Learning with R

Product type Book
Published in May 2019
Publisher
ISBN-13 9781838556334
Pages 502 pages
Edition 1st Edition
Languages
Authors (2):
Karthik Ramasubramanian Karthik Ramasubramanian
Profile icon Karthik Ramasubramanian
Jojo Moolayil Jojo Moolayil
Profile icon Jojo Moolayil
View More author details
Toc

Table of Contents (12) Chapters close

Applied Supervised Learning with R
Preface
1. R for Advanced Analytics 2. Exploratory Analysis of Data 3. Introduction to Supervised Learning 4. Regression 5. Classification 6. Feature Selection and Dimensionality Reduction 7. Model Improvements 8. Model Deployment 9. Capstone Project - Based on Research Papers Appendix

How Does Logistic Regression Work?


Just like linear regression, where the beta coefficients for the variables are estimated using the Ordinary Least Squares (OLS) method, a logistic regression model leverages the maximum-likelihood estimation (MLE). The MLE function estimates the best set of values of the model parameters or beta coefficients such that it maximizes the likelihood function, that is, the probability estimates, which can be also defined as the agreement of the selected model with the observed data. When the best set of parameter values are estimated, plugging these values or beta coefficients into the model equation as previously defined would help in estimating the probability of the outcome for a given sample. Akin to OLS, MLE is also an iterative process.

Let's see a logistic regression model in action on our dataset. To get started, we will use only a small subset of variables for the model. Ideally, it is recommended to start with the most important variables based on the...

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