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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

Residual versus Leverage


If there are any influential points in the data, the Residual versus Leverage plot helps in identifying it. It's common to think that all outlier points are influential, that is, it decides how the regression line comes out. However, not all outliers are influential points. Even if a point is within a reasonable range of values (not an outlier), it could still be an influential point.

In the next plot, we will look out for far off values at the top-right corner or at the bottom-right corner. Those regions are the spaces where observation can be influential in contrast to a regression line. In Figure 4.7, the observations of the red dashed line with high Cook's distance are influential for the regression results. The regression results will be changed if we remove those observations. In the following figure, the bottom plot shows that observation 40 and 39 outside of the dashed line (high Cook's distance). Note that these observations are consistently appearing in...

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