Book Image

Machine Learning with R Quick Start Guide

By : Iván Pastor Sanz
Book Image

Machine Learning with R Quick Start Guide

By: Iván Pastor Sanz

Overview of this book

Machine Learning with R Quick Start Guide takes you on a data-driven journey that starts with the very basics of R and machine learning. It gradually builds upon core concepts so you can handle the varied complexities of data and understand each stage of the machine learning pipeline. From data collection to implementing Natural Language Processing (NLP), this book covers it all. You will implement key machine learning algorithms to understand how they are used to build smart models. You will cover tasks such as clustering, logistic regressions, random forests, support vector machines, and more. Furthermore, you will also look at more advanced aspects such as training neural networks and topic modeling. By the end of the book, you will be able to apply the concepts of machine learning, deal with data-related problems, and solve them using the powerful yet simple language that is R.
Table of Contents (9 chapters)

Wrapper methods

As stated at the beginning of this section, wrapper methods evaluate subsets of variables to detect the possible interactions between variables being a step ahead of the filter methods.

In wrapper methods, several combinations of variables are used in a predictive model and a score is given to each combination according to the model accuracy.

In wrapper methods, a classifier is iteratively trained with multiple combinations of variables acting as a black box, for which the only output is a ranking of important features.

Boruta package

One of the most known wrapper packages in R is called Boruta. This package is mainly based on the algorithm of random forests.

Although this algorithm will be explained in more...