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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 Fitted Plot


This type of plot is between the fitted values and the residual (difference between actual and fitted values) from the lm() method. If the predictor and target variables have a non-linear relationship, the plot will help us identify.

In the following figure, the top plot shows the point scattered all around and the linear relationship between the predictor and target variable is clearly captured. In the bottom plot, the unexplained non-linear relationship is left out in the residuals, and hence the curve. The bottom plot clearly shows it is not the right fit for a linear regression model, a violation of the linear relationship between the predictor and target variable:

Figure 4.4: [Top] Residual versus fitted plot of the linear function. [Bottom] Residual versus fitted plot of the quadratic function

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