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

NCCTG Lung Cancer Data


NCCTG Lung Cancer Data from survival in patients with advanced lung cancer is from the North Central Cancer Treatment Group. The data is a collection of few metadata, such as which institution collected it, age of the patient, sex, and so on. The performance scores in this dataset rates how well the patient can perform the daily activities. The most important variable in any survival analysis dataset is the knowledge about the time-to-event, for example, time until death.

Survival analysis is usually defined as a set of methods for examining data where the outcome variable is the time till the incidence of an event of interest.

Figure 4.21: Variables and its descriptions of North Central Cancer Treatment Group

In the next exercise, we will learn how to create the survival object using the method Surv from the survival package. Note that in the summary of the dataset after adding the survival object, two additional variables SurvObject.time and SurvObject.status are created...