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

Cox Proportional-Hazards Regression Model


The basis for the Cox regression models comes from the survival analysis, a set of statistical methods helpful in investigating the time it takes for an event to occur. Some examples are as follows:

  • Time until a lead is converted to sales

  • Time until a product failure from the start of usage

  • Time after the start of the insurance policy until death

  • Time after diagnosing until death

  • Time until a warranty is claimed for a product

  • Time from customer registration

All these examples are some of the use cases of survival analysis. In most of the survival analysis, there are three wide-spread methods used for carrying out such time-to-event analysis:

  • Kaplan-Meier survival curves for analysis of different groups

  • The logrank test for comparing two or more survival curves

  • Cox proportional hazards regression to describe the effect of variables on survival

Keeping in mind the scope of this chapter and book, we will focus only on the Cox proportional hazards regression. The...