Book Image

Practical Machine Learning with R

By : Brindha Priyadarshini Jeyaraman, Ludvig Renbo Olsen, Monicah Wambugu
Book Image

Practical Machine Learning with R

By: Brindha Priyadarshini Jeyaraman, Ludvig Renbo Olsen, Monicah Wambugu

Overview of this book

With huge amounts of data being generated every moment, businesses need applications that apply complex mathematical calculations to data repeatedly and at speed. With machine learning techniques and R, you can easily develop these kinds of applications in an efficient way. Practical Machine Learning with R begins by helping you grasp the basics of machine learning methods, while also highlighting how and why they work. You will understand how to get these algorithms to work in practice, rather than focusing on mathematical derivations. As you progress from one chapter to another, you will gain hands-on experience of building a machine learning solution in R. Next, using R packages such as rpart, random forest, and multiple imputation by chained equations (MICE), you will learn to implement algorithms including neural net classifier, decision trees, and linear and non-linear regression. As you progress through the book, you’ll delve into various machine learning techniques for both supervised and unsupervised learning approaches. In addition to this, you’ll gain insights into partitioning the datasets and mechanisms to evaluate the results from each model and be able to compare them. By the end of this book, you will have gained expertise in solving your business problems, starting by forming a good problem statement, selecting the most appropriate model to solve your problem, and then ensuring that you do not overtrain it.
Table of Contents (8 chapters)

Types of Features

We have two types of features:

  • Generic features, or datatype-specific features: These are features based on the datatype of the field.
  • Domain-specific features: These are features that are dependent upon the domain of the data. Here, we derive some features from the data based on our business knowledge or the domain.

Datatype-Based Features

Features can be extracted from the existing features. For instance, when we consider a date variable, we can extract the year from the entire date. From these datatypes, it is essential to extract the feature.

Date and Time Features

Imagine that you have a dataset containing information such as dates, months, and years in a non-numerical format; for example, 31/05/2019. We cannot feed this information to a machine learning algorithm, as such algorithms will not understand date-type values. Thus, converting date and time into machine-readable data format is an important skill for a machine learning engineer.

We can extract...