Book Image

Mastering Machine Learning with R - Third Edition

By : Cory Lesmeister
Book Image

Mastering Machine Learning with R - Third Edition

By: Cory Lesmeister

Overview of this book

Given the growing popularity of the R-zerocost statistical programming environment, there has never been a better time to start applying ML to your data. This book will teach you advanced techniques in ML ,using? the latest code in R 3.5. You will delve into various complex features of supervised learning, unsupervised learning, and reinforcement learning algorithms to design efficient and powerful ML models. This newly updated edition is packed with fresh examples covering a range of tasks from different domains. Mastering Machine Learning with R starts by showing you how to quickly manipulate data and prepare it for analysis. You will explore simple and complex models and understand how to compare them. You’ll also learn to use the latest library support, such as TensorFlow and Keras-R, for performing advanced computations. Additionally, you’ll explore complex topics, such as natural language processing (NLP), time series analysis, and clustering, which will further refine your skills in developing applications. Each chapter will help you implement advanced ML algorithms using real-world examples. You’ll even be introduced to reinforcement learning, along with its various use cases and models. In the concluding chapters, you’ll get a glimpse into how some of these blackbox models can be diagnosed and understood. By the end of this book, you’ll be equipped with the skills to deploy ML techniques in your own projects or at work.

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Table of Contents (16 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Preparing and Understanding Data
Preparing and Understanding Data
Overview
Reading the data
Handling duplicate observations
Handling missing values
Zero and near-zero variance features
Treating the data
Summary
2
Linear Regression
Linear Regression
Univariate linear regression
Multivariate linear regression
Summary
3
Logistic Regression
Logistic Regression
Classification methods and linear regression
Logistic regression
Model training and evaluation
Summary
4
Advanced Feature Selection in Linear Models
Advanced Feature Selection in Linear Models
Regularization overview
Data creation
Modeling and evaluation
Summary
5
K-Nearest Neighbors and Support Vector Machines
K-Nearest Neighbors and Support Vector Machines
K-nearest neighbors
Support vector machines
Manipulating data
Modeling and evaluation
Summary
6
Tree-Based Classification
Tree-Based Classification
An overview of the techniques
Datasets and modeling
Summary
7
Neural Networks and Deep Learning
Neural Networks and Deep Learning
Introduction to neural networks
Deep learning – a not-so-deep overview
Creating a simple neural network
An example of deep learning
Summary
8
Creating Ensembles and Multiclass Methods
Creating Ensembles and Multiclass Methods
Ensembles
Data understanding
Modeling and evaluation
Summary
9
Cluster Analysis
Cluster Analysis
Hierarchical clustering
K-means clustering
Gower and PAM
Random forest
Dataset background
Data understanding and preparation
Modeling
Summary
10
Principal Component Analysis
Principal Component Analysis
An overview of the principal components
Data
PCA modeling
Summary
11
Association Analysis
Association Analysis
An overview of association analysis
Data understanding
Data preparation
Modeling and evaluation
Summary
12
Time Series and Causality
Time Series and Causality
Univariate time series analysis
Time series data
Modeling and evaluation
Summary
13
Text Mining
Text Mining
Text mining framework and methods
Data overview
Word frequency
Sentiment analysis
N-grams
Topic models
Classifying text
Additional quantitative analysis
Summary
14
Creating a Package
Creating a Package
Creating a new package
Summary
15
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Logistic regression

As previously discussed, our classification problem is best modeled with the probabilities that are bound by 0 and 1. We can do this for all of our observations with some different functions, but here we'll focus on the logistic function. The logistic function used in logistic regression is as follows:

If you've ever placed a friendly wager on horse races or the World Cup, you may understand the concept better as odds. The logistic function can be turned to odds with the formulation of Probability (Y) / 1 - Probability (Y). For instance, if the probability of Brazil winning the World Cup is 20 percent, then the odds are 0.2 / 1 - 0.2, which is equal to 0.25, translating to odds of one in four.

To translate the odds back to probability, take the odds and divide by one plus the odds. The World Cup example is hence 0.25 / 1 + 0.25, which is equal to...

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