Book Image

Applied Supervised Learning with R

By : Karthik Ramasubramanian, Jojo Moolayil
Book Image

Applied Supervised Learning with R

By: Karthik Ramasubramanian, Jojo Moolayil

Overview of this book

R provides excellent visualization features that are essential for exploring data before using it in automated learning. Applied Supervised Learning with R helps you cover the complete process of employing R to develop applications using supervised machine learning algorithms for your business needs. The book starts by helping you develop your analytical thinking to create a problem statement using business inputs and domain research. You will then learn different evaluation metrics that compare various algorithms, and later progress to using these metrics to select the best algorithm for your problem. After finalizing the algorithm you want to use, you will study the hyperparameter optimization technique to fine-tune your set of optimal parameters. The book demonstrates how you can add different regularization terms to avoid overfitting your model. By the end of this book, you will have gained the advanced skills you need for modeling a supervised machine learning algorithm that precisely fulfills your business needs.
Table of Contents (12 chapters)
Applied Supervised Learning with R
Preface

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...