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Applied Supervised Learning with R

You're reading from   Applied Supervised Learning with R Use machine learning libraries of R to build models that solve business problems and predict future trends

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Product type Paperback
Published in May 2019
Publisher
ISBN-13 9781838556334
Length 502 pages
Edition 1st Edition
Languages
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Authors (2):
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Jojo Moolayil Jojo Moolayil
Author Profile Icon Jojo Moolayil
Jojo Moolayil
Karthik Ramasubramanian Karthik Ramasubramanian
Author Profile Icon Karthik Ramasubramanian
Karthik Ramasubramanian
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Table of Contents (12) Chapters Close

Applied Supervised Learning with R
Preface
1. R for Advanced Analytics FREE CHAPTER 2. Exploratory Analysis of Data 3. Introduction to Supervised Learning 4. Regression 5. Classification 6. Feature Selection and Dimensionality Reduction 7. Model Improvements 8. Model Deployment 9. Capstone Project - Based on Research Papers Appendix

Highly Correlated Variables


Generally, two highly correlated variables likely contribute to the prediction ability of the model, which makes one redundant. For example, if we have a dataset with age, height, and BMI as variables, we know that BMI is a function of age and height and it will always be highly correlated with the other two. If it's not, then something is wrong with the BMI calculation. In such cases, one might decide to remove the other two. However, it is always not this straight. In certain cases, a pair of variables might be highly correlated, but it is not easy to interpret why that is the case. In such cases, one can randomly drop one of the two.

Exercise 82: Plotting a Correlated Matrix

In this exercise, we will compute the correlation between a pair of variables and draw a correlation plot using the corrplot package.

Perform the following steps to complete the exercise:

  1. Import the required libraries using the following command:

    library(mlbench)
    library(caret)

    The output is...

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