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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

Data understanding

After enduring the all-important pain of the first step, you can now get your hands on the data. The tasks in this process consist of the following:

Collect the data
Describe the data
Explore the data
Verify the data quality

This step is the classic case of ETL is Extract, Transform, Load. There are some considerations here. You need to make an initial determination that the data available is adequate to meet your analytical needs. As you explore the data, visually and otherwise, determine if the variables are sparse and identify the extent to which the data may be missing. This may drive the learning method that you use and/or whether the imputation of the missing data is necessary and feasible.

Verifying the data quality is critical. Take the time to understand who collects the data, how it is collected, and even why it is collected. It is likely that you may stumble upon an incomplete data collection, cases where unintended IT issues led to errors in the data, or there were planned changes in the business rules. This is critical in the time series where often business rules change over time on how the data is classified. Finally, it is a good idea to begin documenting any code at this step. As a part of the documentation process, if a data dictionary is not available, save yourself the heartache later on and make one.

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Tech Concepts

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Programming languages

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