Book Image

Statistics for Data Science

Book Image

Statistics for Data Science

Overview of this book

Data science is an ever-evolving field, which is growing in popularity at an exponential rate. Data science includes techniques and theories extracted from the fields of statistics; computer science, and, most importantly, machine learning, databases, data visualization, and so on. This book takes you through an entire journey of statistics, from knowing very little to becoming comfortable in using various statistical methods for data science tasks. It starts off with simple statistics and then move on to statistical methods that are used in data science algorithms. The R programs for statistical computation are clearly explained along with logic. You will come across various mathematical concepts, such as variance, standard deviation, probability, matrix calculations, and more. You will learn only what is required to implement statistics in data science tasks such as data cleaning, mining, and analysis. You will learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks. By the end of the book, you will be comfortable with performing various statistical computations for data science programmatically.
Table of Contents (19 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Using R to illustrate boosting methods


In order to further illustrate the use of boosting, we should have an example.

In this section, we'll take a high-level look at a thought-provoking prediction problem drawn from Mastering Predictive Analytics with R, Second Edition, James D. Miller and Rui Miguel Forte, August 2017 (https://www.packtpub.com/big-data-and-business-intelligence/mastering-predictive-analytics-r-second-edition).

In this original example, patterns made by radiation on a telescope camera are analyzed in an attempt to predict whether a certain pattern came from gamma rays leaking into the atmosphere or from regular background radiation.

Gamma rays leave distinctive elliptical patterns and so we can create a set of features to describe these. The dataset used is the MAGIC Gamma Telescope Data Set, hosted by the UCI Machine Learning Repository at http://archive.ics.uci.edu/ml/datasets/MAGIC+Gamma+Telescope.

This data consists of 19,020 observations, holding the following list of...