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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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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

Advanced Operations on Data Frames

In the previous chapter, we performed a number of operations on data frames, including rbind(). There are many more operations that can be performed on data frames, which are very useful while preparing the data for the model. The following exercises will describe these operations in detail and illustrate them through their corresponding implementation in R:

  • The order function: The order function is used to sort a data frame. We can specify ascending or descending order using the "-" symbol.
  • The sort function: The sort function can also be used to sort the data. The order can be specified as "decreasing=TRUE" or "decreasing"="FALSE".
  • The rank function: The rank function is used to rank the values in the data in a numerical manner.

Sorting, ordering, and ranking are operations that act as techniques to identify outliers. Outliers are values that are either too big or too small and do not fit in the value...

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Practical Machine Learning with R
Published in: Aug 2019
Publisher: Packt
ISBN-13: 9781838550134
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