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Practical Machine Learning with R

You're reading from   Practical Machine Learning with R Define, build, and evaluate machine learning models for real-world applications

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Product type Paperback
Published in Aug 2019
Publisher Packt
ISBN-13 9781838550134
Length 416 pages
Edition 1st Edition
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Authors (3):
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Brindha Priyadarshini Jeyaraman Brindha Priyadarshini Jeyaraman
Author Profile Icon Brindha Priyadarshini Jeyaraman
Brindha Priyadarshini Jeyaraman
Ludvig Renbo Olsen Ludvig Renbo Olsen
Author Profile Icon Ludvig Renbo Olsen
Ludvig Renbo Olsen
Monicah Wambugu Monicah Wambugu
Author Profile Icon Monicah Wambugu
Monicah Wambugu
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Toc

Table of Contents (8) Chapters Close

About the Book 1. An Introduction to Machine Learning FREE CHAPTER 2. Data Cleaning and Pre-processing 3. Feature Engineering 4. Introduction to neuralnet and Evaluation Methods 5. Linear and Logistic Regression Models 6. Unsupervised Learning 1. Appendix

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.

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