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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

Scale-Location Plot


Scale-Location plot shows whether residuals are spread equally along the ranges of input variables (predictor). The assumption of equal variance (homoscedasticity) could also be checked with this plot. If we see a horizontal line with randomly spread points, it means that the model is good.

The plot is between fitted values and the square root of standardized residuals. In the following figure, the top plot shows the linear function, and the residuals are spread randomly along the horizontal lines, whereas in the bottom plot, there seems to be a pattern that is not random. Hence, the variance is not equal:

Figure 4.6: [Top] Scale-Location plot of the linear function. [Bottom] Scale-Location plot of the quadratic function

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