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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

Deployment

If everything is done according to the plan up to this point, it might just come down to flipping a switch and your model goes live. Assuming that this is not the case, here are the tasks of this step:

Deploying the plan
Monitoring and maintenance of the plan
Producing the final report
Reviewing the project

After the deployment and monitoring/maintenance is underway, it is crucial for yourself and those that will walk in your steps to produce a well-written final report. This report should include a white paper and briefing slide. I have to say that I resisted the drive to put my findings in a white paper as I was an indentured servant to the military's passion for PowerPoint slides. However, slides can and will be used against you, cherry-picked or misrepresented by various parties for their benefit. Trust me, that just doesn't happen with a white paper as it becomes an extension of your findings and beliefs.

Now for the all-important process review. You may have your own proprietary way of conducting it, but here is what it should cover, whether you conduct it in a formal or informal way:

What was the plan?
What actually happened?
Why did it happen or did not happen?
What should be sustained in future projects?
What should be improved upon in future projects?
Create an action plan to ensure sustainment and improvement happens

That concludes the review of the CRISP-DM process, which provides a comprehensive and flexible framework to guarantee the success of your project and make you an agent of change.

CONTINUE READING

83

Tech Concepts

36

Programming languages

73

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