Book Image

Mastering Predictive Analytics with R - Second Edition

By : James D. Miller, Rui Miguel Forte
Book Image

Mastering Predictive Analytics with R - Second Edition

By: James D. Miller, Rui Miguel Forte

Overview of this book

R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions. With its constantly growing community and plethora of packages, R offers the functionality to deal with a truly vast array of problems. The book begins with a dedicated chapter on the language of models and the predictive modeling process. You will understand the learning curve and the process of tidying data. Each subsequent chapter tackles a particular type of model, such as neural networks, and focuses on the three important questions of how the model works, how to use R to train it, and how to measure and assess its performance using real-world datasets. How do you train models that can handle really large datasets? This book will also show you just that. Finally, you will tackle the really important topic of deep learning by implementing applications on word embedding and recurrent neural networks. By the end of this book, you will have explored and tested the most popular modeling techniques in use on real- world datasets and mastered a diverse range of techniques in predictive analytics using R.

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Table of Contents (22 chapters)
Mastering Predictive Analytics with R Second Edition
Mastering Predictive Analytics with R Second Edition
Credits
Credits
About the Authors
About the Authors
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Customer Feedback
Customer Feedback
Preface
Preface
Free Chapter
1
Gearing Up for Predictive Modeling
Gearing Up for Predictive Modeling
Models
Types of model
The process of predictive modeling
Summary
2
Tidying Data and Measuring Performance
Tidying Data and Measuring Performance
Getting started
Tidying data
Categorizing data quality
Performance metrics
Cross-validation
Learning curves
Summary
3
Linear Regression
Linear Regression
Introduction to linear regression
Simple linear regression
Multiple linear regression
Assessing linear regression models
Problems with linear regression
Feature selection
Regularization
Polynomial regression
Summary
4
Generalized Linear Models
Generalized Linear Models
Classifying with linear regression
Introduction to logistic regression
Predicting heart disease
Assessing logistic regression models
Regularization with the lasso
Classification metrics
Extensions of the binary logistic classifier
Poisson regression
Negative Binomial regression
Summary
5
Neural Networks
Neural Networks
The biological neuron
The artificial neuron
Stochastic gradient descent
Multilayer perceptron networks
The back propagation algorithm
Predicting the energy efficiency of buildings
Predicting glass type revisited
Predicting handwritten digits
Radial basis function networks
Summary
6
Support Vector Machines
Support Vector Machines
Maximal margin classification
Support vector classification
Kernels and support vector machines
Predicting chemical biodegration
Predicting credit scores
Multiclass classification with support vector machines
Summary
7
Tree-Based Methods
Tree-Based Methods
The intuition for tree models
Algorithms for training decision trees
Predicting class membership on synthetic 2D data
Predicting the authenticity of banknotes
Predicting complex skill learning
Improvements to the M5 model
Summary
8
Dimensionality Reduction
Dimensionality Reduction
Defining DR
Summary
9
Ensemble Methods
Ensemble Methods
Bagging
Boosting
Predicting atmospheric gamma ray radiation
Predicting complex skill learning with boosting
Summary
10
Probabilistic Graphical Models
Probabilistic Graphical Models
A little graph theory
Bayes' theorem
Conditional independence
Bayesian networks
The Naïve Bayes classifier
Summary
11
Topic Modeling
Topic Modeling
An overview of topic modeling
Latent Dirichlet Allocation
Modeling the topics of online news stories
Modeling tweet topics
Summary
12
Recommendation Systems
Recommendation Systems
Rating matrix
Collaborative filtering
Singular value decomposition
Predicting recommendations for movies and jokes
Loading and pre-processing the data
Exploring the data
Other approaches to recommendation systems
Summary
13
Scaling Up
Scaling Up
Starting the project
Characteristics of big data
Training models at scale
A path forward
Alternatives
Summary
14
Deep Learning
Deep Learning
Machine learning or deep learning
What is deep learning?
Summary
Index
Index

Predicting glass type revisited

In Chapter 3, Linear Regression, we analyzed the glass identification dataset, whose task is to identify the type of glass comprising a glass fragment found at a crime scene. The output of this dataset is a factor with several class levels corresponding to different types of glass. Our previous approach was to build a one-versus-all model using multinomial logistic regression. The results were not very promising, and one of the main points of concern was a poor model fit on the training data.

In this section, we will revisit this dataset and see whether a neural network model can do better. At the same time, we will demonstrate how neural networks can handle classification problems as well:

> glass <- read.csv("glass.data", header = FALSE)
> names(glass) <- c("id", "RI", "Na", "Mg", "Al", "Si", "K", "Ca", 
"Ba", "Fe", "Type")
> glass$id <- NULL

Our output is a multiclass factor and so we will want to dummy-encode this into binary columns. With...

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