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.
Free Chapter
An Introduction to Machine Learning
Data Cleaning and Pre-processing
Feature Engineering
Introduction to neuralnet and Evaluation Methods
Linear and Logistic Regression Models
Unsupervised Learning

Derived Features or Domain-Specific Features

These are features that are derived from data that requires an understanding of the business domain.

Let's imagine a dataset that contains data for the sale prices of houses in different areas of a city and that our goal is to predict the future price of any house. For this dataset, the input fields are area code, size of the house, floor number, type of house (individual/apartment), age of the property, renovated status, and so on, along with the sale price of the house. The derived features in this scenario are as follows:

• Total sales in the area for the past week, month, and so on
• Location of the house (central area or suburb, based on the area code)
• Livability index (based on the age and renovated columns)

Another example of deriving domain-specific features would be deriving a person's age from their birth date and the current date in a dataset containing information about people.