Book Image

Machine Learning with R - Fourth Edition

By : Brett Lantz
5 (1)
Book Image

Machine Learning with R - Fourth Edition

5 (1)
By: Brett Lantz

Overview of this book

Dive into R with this data science guide on machine learning (ML). Machine Learning with R, Fourth Edition, takes you through classification methods like nearest neighbor and Naive Bayes and regression modeling, from simple linear to logistic. Dive into practical deep learning with neural networks and support vector machines and unearth valuable insights from complex data sets with market basket analysis. Learn how to unlock hidden patterns within your data using k-means clustering. With three new chapters on data, you’ll hone your skills in advanced data preparation, mastering feature engineering, and tackling challenging data scenarios. This book helps you conquer high-dimensionality, sparsity, and imbalanced data with confidence. Navigate the complexities of big data with ease, harnessing the power of parallel computing and leveraging GPU resources for faster insights. Elevate your understanding of model performance evaluation, moving beyond accuracy metrics. With a new chapter on building better learners, you’ll pick up techniques that top teams use to improve model performance with ensemble methods and innovative model stacking and blending techniques. Machine Learning with R, Fourth Edition, equips you with the tools and knowledge to tackle even the most formidable data challenges. Unlock the full potential of machine learning and become a true master of the craft.

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Table of Contents (18 chapters)
Preface
Preface
Who this book is for
What this book covers
What you need for this book
Get in touch
1
Introducing Machine Learning
Introducing Machine Learning
The origins of machine learning
Uses and abuses of machine learning
How machines learn
Machine learning in practice
Machine learning with R
Summary
Free Chapter
2
Managing and Understanding Data
Managing and Understanding Data
R data structures
Managing data with R
Exploring and understanding data
Summary
3
Lazy Learning – Classification Using Nearest Neighbors
Lazy Learning – Classification Using Nearest Neighbors
Understanding nearest neighbor classification
Example – diagnosing breast cancer with the k-NN algorithm
Summary
4
Probabilistic Learning – Classification Using Naive Bayes
Probabilistic Learning – Classification Using Naive Bayes
Understanding Naive Bayes
Example – filtering mobile phone spam with the Naive Bayes algorithm
Summary
5
Divide and Conquer – Classification Using Decision Trees and Rules
Divide and Conquer – Classification Using Decision Trees and Rules
Understanding decision trees
Example – identifying risky bank loans using C5.0 decision trees
Understanding classification rules
Example – identifying poisonous mushrooms with rule learners
Summary
6
Forecasting Numeric Data – Regression Methods
Forecasting Numeric Data – Regression Methods
Understanding regression
Example – predicting auto insurance claims costs using linear regression
Understanding regression trees and model trees
Example – estimating the quality of wines with regression trees and model trees
Summary
7
Black-Box Methods – Neural Networks and Support Vector Machines
Black-Box Methods – Neural Networks and Support Vector Machines
Understanding neural networks
Example – modeling the strength of concrete with ANNs
Understanding support vector machines
Example – performing OCR with SVMs
Summary
8
Finding Patterns – Market Basket Analysis Using Association Rules
Finding Patterns – Market Basket Analysis Using Association Rules
Understanding association rules
Example – identifying frequently purchased groceries with association rules
Summary
9
Finding Groups of Data – Clustering with k-means
Finding Groups of Data – Clustering with k-means
Understanding clustering
Finding teen market segments using k-means clustering
Summary
10
Evaluating Model Performance
Evaluating Model Performance
Measuring performance for classification
Estimating future performance
Summary
11
Being Successful with Machine Learning
Being Successful with Machine Learning
What makes a successful machine learning practitioner?
What makes a successful machine learning model?
Putting the “science” in data science
Summary
12
Advanced Data Preparation
Advanced Data Preparation
Performing feature engineering
Feature engineering in practice
Exploring R’s tidyverse
Summary
13
Challenging Data – Too Much, Too Little, Too Complex
Challenging Data – Too Much, Too Little, Too Complex
The challenge of high-dimension data
Making use of sparse data
Handling missing data
The problem of imbalanced data
Summary
14
Building Better Learners
Building Better Learners
Tuning stock models for better performance
Improving model performance with ensembles
Stacking models for meta-learning
Summary
15
Making Use of Big Data
Making Use of Big Data
Practical applications of deep learning
Unsupervised learning and big data
Adapting R to handle large datasets
Summary
16
Other Books You May Enjoy
Other Books You May Enjoy
17
Index
Index

Summary

In this chapter, we learned about the basics of managing data in R. We started by taking an in-depth look at the structures used for storing various types of data. The foundational R data structure is the vector, which is extended and combined into more complex data types, such as lists and data frames. The data frame is an R data structure that corresponds to the notion of a dataset having both features and examples. R provides functions for reading and writing data frames to spreadsheet-like tabular data files.

We then explored a real-world dataset containing the prices of used cars. We examined numeric variables using common summary statistics of center and spread, and visualized relationships between prices and odometer readings with a scatterplot. Next, we examined nominal variables using tables. In examining the used car data, we followed an exploratory process that can be used to understand any dataset. These skills will be required for the other projects throughout this book.

Now that we have spent some time understanding the basics of data management with R, you are ready to begin using machine learning to solve real-world problems. In the next chapter, we will tackle our first classification task using nearest neighbor methods. You may be surprised to discover that with just a few lines of R code, a machine can achieve human-like performance on a challenging medical diagnosis task.

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