Machine Learning with R

Expert techniques for predictive modeling, 3rd Edition

Brett Lantz

• Publisher: Packt Publishing
• Year: 2019
• eBook ISBN: 9781788291552
• Edition: 3
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

Solve real-world data problems with R and machine learningKey Features• Third edition of the bestselling, widely acclaimed R machine learning book, updated and improved for R 3.6 and beyond• Harness the power of R to build flexible, effective, and transparent machine learning models• Learn quickly with a clear, hands-on guide by experienced machine learning teacher and practitioner, Brett LantzBook DescriptionMachine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data. Machine Learning with R, Third Edition provides a hands-on, readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights, make new predictions, and visualize your findings. This new 3rd edition updates the classic R data science book to R 3.6 with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Find powerful new insights in your data; discover machine learning with R.What you will learn• Discover the origins of machine learning and how exactly a computer learns by example• Prepare your data for machine learning work with the R programming language• Classify important outcomes using nearest neighbor and Bayesian methods• Predict future events using decision trees, rules, and support vector machines• Forecast numeric data and estimate financial values using regression methods• Model complex processes with artificial neural networks — the basis of deep learning• Avoid bias in machine learning models• Evaluate your models and improve their performance• Connect R to SQL databases and emerging big data technologies such as Spark, H2O, and TensorFlowWho this book is forData scientists, students, and other practitioners who want a clear, accessible guide to machine learning with R.

Machine Learning with R - Third Edition

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Table of Contents

Machine Learning with R - Third Edition

Why subscribe?

Packt.com

Contributors

About the authors

About the reviewer

Preface

Who this book is for

What this book covers

What you need for this book

Download the example code files

Download the color images

Conventions used

Get in touch

Reviews

1. Introducing Machine Learning

The origins of machine learning

Uses and abuses of machine learning

Machine learning successes

The limits of machine learning

Machine learning ethics

How machines learn

Data storage

Abstraction

Generalization

Evaluation

Machine learning in practice

Types of input data

Types of machine learning algorithms

Matching input data to algorithms

Machine learning with R

Installing R packages

Loading and unloading R packages

Installing RStudio

Summary

2. Managing and Understanding Data

R data structures

Vectors

Factors

Lists

Data frames

Matrices and arrays

Managing data with R

Saving, loading, and removing R data structures

Importing and saving data from CSV files

Exploring and understanding data

Exploring the structure of data

Exploring numeric variables

Measuring the central tendency – mean and median

Measuring spread – quartiles and the five-number summary

Visualizing numeric variables – boxplots

Visualizing numeric variables – histograms

Understanding numeric data – uniform and normal distributions

Measuring spread – variance and standard deviation

Exploring categorical variables

Measuring the central tendency – the mode

Exploring relationships between variables

Visualizing relationships – scatterplots

Examining relationships – two-way cross-tabulations

Summary

3. Lazy Learning – Classification Using Nearest Neighbors

Understanding nearest neighbor classification

The k-NN algorithm

Measuring similarity with distance

Choosing an appropriate k

Preparing data for use with k-NN

Why is the k-NN algorithm lazy?

Example – diagnosing breast cancer with the k-NN algorithm

Step 1 – collecting data

Step 2 – exploring and preparing the data

Transformation – normalizing numeric data

Data preparation – creating training and test datasets

Step 3 – training a model on the data

Step 4 – evaluating model performance

Step 5 – improving model performance

Transformation – z-score standardization

Testing alternative values of k

Summary

4. Probabilistic Learning – Classification Using Naive Bayes

Understanding Naive Bayes

Basic concepts of Bayesian methods

Understanding probability

Understanding joint probability

Computing conditional probability with Bayes' theorem

The Naive Bayes algorithm

Classification with Naive Bayes

The Laplace estimator

Using numeric features with Naive Bayes

Example – filtering mobile phone spam with the Naive Bayes algorithm

Step 1 – collecting data

Step 2 – exploring and preparing the data

Data preparation – cleaning and standardizing text data

Data preparation – splitting text documents into words

Data preparation – creating training and test datasets

Visualizing text data – word clouds

Data preparation – creating indicator features for frequent words

Step 3 – training a model on the data

Step 4 – evaluating model performance

Step 5 – improving model performance

Summary

5. Divide and Conquer – Classification Using Decision Trees and Rules

Understanding decision trees

Divide and conquer

The C5.0 decision tree algorithm

Choosing the best split

Pruning the decision tree

Example – identifying risky bank loans using C5.0 decision trees

Step 1 – collecting data

Step 2 – exploring and preparing the data

Data preparation – creating random training and test datasets

Step 3 – training a model on the data

Step 4 – evaluating model performance

Step 5 – improving model performance

Boosting the accuracy of decision trees

Making some mistakes cost more than others

Understanding classification rules

Separate and conquer

The 1R algorithm

The RIPPER algorithm

Rules from decision trees

What makes trees and rules greedy?

Example – identifying poisonous mushrooms with rule learners

Step 1 – collecting data

Step 2 – exploring and preparing the data

Step 3 – training a model on the data

Step 4 – evaluating model performance

Step 5 – improving model performance

Summary

6. Forecasting Numeric Data – Regression Methods

Understanding regression

Simple linear regression

Ordinary least squares estimation

Correlations

Multiple linear regression

Example – predicting medical expenses using linear regression

Step 1 – collecting data

Step 2 – exploring and preparing the data

Exploring relationships among features – the correlation matrix

Visualizing relationships among features – the scatterplot matrix

Step 3 – training a model on the data

Step 4 – evaluating model performance

Step 5 – improving model performance

Model specification – adding nonlinear relationships

Transformation – converting a numeric variable to a binary indicator

Model specification – adding interaction effects

Putting it all together – an improved regression model

Making predictions with a regression model

Understanding regression trees and model trees

Adding regression to trees

Example – estimating the quality of wines with regression trees and model trees

Step 1 – collecting data

Step 2 – exploring and preparing the data

Step 3 – training a model on the data

Visualizing decision trees

Step 4 – evaluating model performance

Measuring performance with the mean absolute error

Step 5 – improving model performance

Summary

7. Black Box Methods – Neural Networks and Support Vector Machines

Understanding neural networks

From biological to artificial neurons

Activation functions

Network topology

The number of layers

The direction of information travel

The number of nodes in each layer

Training neural networks with backpropagation

Example – modeling the strength of concrete with ANNs

Step 1 – collecting data

Step 2 – exploring and preparing the data

Step 3 – training a model on the data

Step 4 – evaluating model performance

Step 5 – improving model performance

Understanding support vector machines

Classification with hyperplanes

The case of linearly separable data

The case of nonlinearly separable data

Using kernels for nonlinear spaces

Example – performing OCR with SVMs

Step 1 – collecting data

Step 2 – e...

Table of contents

• Machine Learning with R - Third Edition