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Statistics for Data Science

You're reading from Statistics for Data Science Leverage the power of statistics for Data Analysis, Classification, Regression, Machine Learning, and Neural Networks

Product type Paperback

Published in Nov 2017

Publisher Packt

ISBN-13 9781788290678

Length 286 pages

Edition 1st Edition

Languages

R

Tools

RStudio

Concepts

Data Analysis

Author (1):

James D. Miller

View More author details

Table of Contents (13) Chapters

Preface

1. Transitioning from Data Developer to Data Scientist

2. Declaring the Objectives FREE CHAPTER

3. A Developer's Approach to Data Cleaning

4. Data Mining and the Database Developer

5. Statistical Analysis for the Database Developer

6. Database Progression to Database Regression

7. Regularization for Database Improvement

8. Database Development and Assessment

9. Databases and Neural Networks

10. Boosting your Database

11. Database Classification using Support Vector Machines

12. Database Structures and Machine Learning

Statistical regularization

So, what is statistical regularization?

With regularization, whether we are speaking about mathematics, statistics, or machine learning, we are essentially talking about a process of adding additional information in order to solve a problem.

The term regularization has been described as an abstract concept of management of complex systems (according to a set of rules or accepted concepts). These rules will define how one can add or modify values in order to satisfy a requirement or solve a problem.

Does adding or modifying values mean changing data? (More about this will be studied later in this chapter.)

Various statistical regularization methods

Within the statistical community, the most popular statistical regularization methods may include the following:

Ridge
Lasso
Least angles

Ridge

Ridge regression is a statistical technique that is used when analyzing regression data or models that suffer from a condition known as multicollinearity. When multicollinearity occurs...

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Authors (1)

James D. Miller

James D. Miller

James D. Miller is an IBM certified expert, Master Consultant, Application/System Architect with +35 years of applications & system design/development experience across multiple platforms, technologies and data formats, including Big Data. His experience includes IBM Planning Analytics, BI, Web architecture & design, systems analysis, GUI design & testing, Data modeling, design, and development of OLAP, Client/Server, Web & Mainframe applications and systems utilizing: Planning Analytics Workspace (PAW), IBM Watson Analytics, Cognos BI & TM1, Framework Manager, dynaSight/ArcPlan, ASP, DHTML, XML, MS Visual Basic, VBA, PERL, R, SPLUNK, MS SQL Server, ORACLE, etc. He has authored numerous books, including Implementing Splunk - Second Edition; Mastering Splunk; Hands-On Machine Learning with IBM Watson; IBM Watson Projects; Statistics for Data Science; Mastering Predictive Analytics with R - Second Edition and others. Project areas include those with Data Analytics, Planning Analytics, and FOPM projects, holding various roles from architect, developer, technical and project leader.

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