Book Image

Applied Supervised Learning with R

By : Karthik Ramasubramanian, Jojo Moolayil
Book Image

Applied Supervised Learning with R

By: Karthik Ramasubramanian, Jojo Moolayil

Overview of this book

R provides excellent visualization features that are essential for exploring data before using it in automated learning. Applied Supervised Learning with R helps you cover the complete process of employing R to develop applications using supervised machine learning algorithms for your business needs. The book starts by helping you develop your analytical thinking to create a problem statement using business inputs and domain research. You will then learn different evaluation metrics that compare various algorithms, and later progress to using these metrics to select the best algorithm for your problem. After finalizing the algorithm you want to use, you will study the hyperparameter optimization technique to fine-tune your set of optimal parameters. The book demonstrates how you can add different regularization terms to avoid overfitting your model. By the end of this book, you will have gained the advanced skills you need for modeling a supervised machine learning algorithm that precisely fulfills your business needs.
Table of Contents (12 chapters)
Applied Supervised Learning with R
Preface

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...