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Mastering Machine Learning with R

By : Cory Lesmeister

4.3 (6)

Mastering Machine Learning with R

4.3 (6)

By: Cory Lesmeister

Overview of this book

Machine learning is a field of Artificial Intelligence to build systems that learn from data. Given the growing prominence of R—a cross-platform, zero-cost statistical programming environment—there has never been a better time to start applying machine learning to your data. The book starts with introduction to Cross-Industry Standard Process for Data Mining. It takes you through Multivariate Regression in detail. Moving on, you will also address Classification and Regression trees. You will learn a couple of “Unsupervised techniques”. Finally, the book will walk you through text analysis and time series. The book will deliver practical and real-world solutions to problems and variety of tasks such as complex recommendation systems. By the end of this book, you will gain expertise in performing R machine learning and will be able to build complex ML projects using R and its packages.

Preface

Preface

Machine learning defined

Machine learning caveats

Failure to engineer features

Overfitting and underfitting

Causality

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. A Process for Success

1. A Process for Success

The process

Business understanding

Data understanding

Data preparation

Modeling

Evaluation

Deployment

Algorithm flowchart

Summary

2. Linear Regression – The Blocking and Tackling of Machine Learning

2. Linear Regression – The Blocking and Tackling of Machine Learning

Univariate linear regression

Multivariate linear regression

Other linear model considerations

Summary

3. Logistic Regression and Discriminant Analysis

3. Logistic Regression and Discriminant Analysis

Classification methods and linear regression

Logistic regression

Model selection

Summary

4. Advanced Feature Selection in Linear Models

4. Advanced Feature Selection in Linear Models

Regularization in a nutshell

Business case

Modeling and evaluation

Model selection

Summary

5. More Classification Techniques – K-Nearest Neighbors and Support Vector Machines

5. More Classification Techniques – K-Nearest Neighbors and Support Vector Machines

K-Nearest Neighbors

Support Vector Machines

Business case

Feature selection for SVMs

Summary

6. Classification and Regression Trees

6. Classification and Regression Trees

Introduction

An overview of the techniques

Business case

Summary

7. Neural Networks

7. Neural Networks

Neural network

Deep learning, a not-so-deep overview

Business understanding

Data understanding and preparation

Modeling and evaluation

An example of deep learning

Summary

8. Cluster Analysis

8. Cluster Analysis

Hierarchical clustering

K-means clustering

Gower and partitioning around medoids

Data understanding and preparation

Modeling and evaluation

Summary

9. Principal Components Analysis

9. Principal Components Analysis

An overview of the principal components

Modeling and evaluation

Summary

10. Market Basket Analysis and Recommendation Engines

10. Market Basket Analysis and Recommendation Engines

An overview of a market basket analysis

Business understanding

Data understanding and preparation

Modeling and evaluation

An overview of a recommendation engine

Business understanding and recommendations

Data understanding, preparation, and recommendations

Modeling, evaluation, and recommendations

Summary

11. Time Series and Causality

11. Time Series and Causality

Univariate time series analysis

Modeling and evaluation

Summary

12. Text Mining

12. Text Mining

Text mining framework and methods

Topic models

Modeling and evaluation

Summary

A. R Fundamentals

A. R Fundamentals

Introduction

Getting R up and running

Using R

Data frames and matrices

Summary stats

Installing and loading the R packages

Summary

Index

Index

The process

The CRISP-DM process was designed specifically for the data mining. However, it is flexible and thorough enough that it can be applied to any analytical project, whether it is predictive analytics, data science, or machine learning. Don't be intimidated by the numerous list of tasks as you can apply your judgment to the process and adapt it for any real-world situation. The following figure provides a visual representation of the process and shows the feedback loops, which facilitate its flexibility:

The process

Figure from CRISP-DM 1.0, Step-by-step data mining guide

The process has the following six phases:

Business Understanding
Data Understanding
Data Preparation
Modeling
Evaluation
Deployment

For an in-depth review of the entire process with all of its tasks and subtasks, you can examine the paper by SPSS, CRISP-DM 1.0, step-by-step data mining guide, available at https://the-modeling-agency.com/crisp-dm.pdf.

I will discuss each of the steps in the process, covering the important tasks. However, it will not be in the detailed level of the guide, but more high level. We will not skip any of the critical details but focus more on the techniques that one can apply to the tasks. Keep in mind that the process steps will be used in the later chapters as a framework in the actual application of the machine learning methods in general and the R code specifically.

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Tech Concepts

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Programming languages

73

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