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

Identifying the Category of Prediction

Based on the category of prediction, we will perform different pre-processing steps. The category of prediction could be any of these:

  • Categorical Prediction: In this type of prediction, the output to be predicted will have class values such as yes, no, or given categories.
  • Numeric Prediction: In a numeric prediction, the output that will be predicted is a numeric value, such as predicting the cost of a house.
You have been reading a chapter from
Practical Machine Learning with R
Published in: Aug 2019
Publisher: Packt
ISBN-13: 9781838550134
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