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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
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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

Normal Q-Q Plot


Q-Q plot, also called Quantile-Quantile plot, supports to check if the data plausibly comes from approximately theoretical distribution; in this instance, Normal Distribution. A Q-Q plot is a scatterplot shaped by plotting two sets of quantiles (points below which a certain proportion of the data falls) in contrast to one another. If both groups of quantiles came from a similar distribution, we must see the points creating a coarsely straight line. Provided a vector of data, the normal Q-Q plot plots the data in sorted order versus quantiles from a standard normal distribution.

The second assumption in linear regression was that all the predictor variables are normally distributed. If it is true, the residuals will also be normally distributed. Normal Q-Q is a plot between standardized residuals and theoretical quantiles. Visually, we can inspect whether the residuals follow the straight line, if it is normally distributed, or if there is any deviation that indicates violation...

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