Mastering Predictive Analytics with R

Master the craft of predictive modeling by developing strategy, intuition, and a solid foundation in essential concepts
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Mastering Predictive Analytics with R

Rui Miguel Forte

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Master the craft of predictive modeling by developing strategy, intuition, and a solid foundation in essential concepts
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Book Details

ISBN 139781783982806
Paperback414 pages

Book Description

R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions in the real world. With its constantly growing community and plethora of packages, R offers the functionality to deal with a truly vast array of problems.

This book is designed to be both a guide and a reference for moving beyond the basics of predictive modeling. The book begins with a dedicated chapter on the language of models and the predictive modeling process. 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 data sets.

By the end of this book, you will have explored and tested the most popular modeling techniques in use on real world data sets and mastered a diverse range of techniques in predictive analytics.

Table of Contents

Chapter 1: Gearing Up for Predictive Modeling
Models
Types of models
The process of predictive modeling
Performance metrics
Summary
Chapter 2: Linear Regression
Introduction to linear regression
Simple linear regression
Multiple linear regression
Assessing linear regression models
Problems with linear regression
Feature selection
Regularization
Summary
Chapter 3: Logistic Regression
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
Summary
Chapter 4: Neural Networks
The biological neuron
The artificial neuron
Stochastic gradient descent
Multilayer perceptron networks
Predicting the energy efficiency of buildings
Predicting glass type revisited
Predicting handwritten digits
Summary
Chapter 5: Support Vector Machines
Maximal margin classification
Support vector classification
Kernels and support vector machines
Predicting chemical biodegration
Cross-validation
Predicting credit scores
Multiclass classification with support vector machines
Summary
Chapter 6: 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
Summary
Chapter 7: Ensemble Methods
Bagging
Boosting
Predicting atmospheric gamma ray radiation
Predicting complex skill learning with boosting
Random forests
Summary
Chapter 8: Probabilistic Graphical Models
A little graph theory
Bayes' Theorem
Conditional independence
Bayesian networks
The Naïve Bayes classifier
Hidden Markov models
Predicting promoter gene sequences
Predicting letter patterns in English words
Summary
Chapter 9: Time Series Analysis
Fundamental concepts of time series
Some fundamental time series
Stationarity
Stationary time series models
Non-stationary time series models
Predicting intense earthquakes
Predicting lynx trappings
Predicting foreign exchange rates
Other time series models
Summary
Chapter 10: Topic Modeling
An overview of topic modeling
Latent Dirichlet Allocation
Modeling the topics of online news stories
Summary
Chapter 11: Recommendation Systems
Rating matrix
Collaborative filtering
Singular value decomposition
R and Big Data
Predicting recommendations for movies and jokes
Loading and preprocessing the data
Exploring the data
Other approaches to recommendation systems
Summary

What You Will Learn

  • Master the steps involved in the predictive modeling process
  • Learn how to classify predictive models and distinguish which models are suitable for a particular problem
  • Understand how and why each predictive model works
  • Recognize the assumptions, strengths, and weaknesses of a predictive model, and that there is no best model for every problem
  • Select appropriate metrics to assess the performance of different types of predictive model
  • Diagnose performance and accuracy problems when they arise and learn how to deal with them
  • Grow your expertise in using R and its diverse range of packages

Authors

Table of Contents

Chapter 1: Gearing Up for Predictive Modeling
Models
Types of models
The process of predictive modeling
Performance metrics
Summary
Chapter 2: Linear Regression
Introduction to linear regression
Simple linear regression
Multiple linear regression
Assessing linear regression models
Problems with linear regression
Feature selection
Regularization
Summary
Chapter 3: Logistic Regression
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
Summary
Chapter 4: Neural Networks
The biological neuron
The artificial neuron
Stochastic gradient descent
Multilayer perceptron networks
Predicting the energy efficiency of buildings
Predicting glass type revisited
Predicting handwritten digits
Summary
Chapter 5: Support Vector Machines
Maximal margin classification
Support vector classification
Kernels and support vector machines
Predicting chemical biodegration
Cross-validation
Predicting credit scores
Multiclass classification with support vector machines
Summary
Chapter 6: 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
Summary
Chapter 7: Ensemble Methods
Bagging
Boosting
Predicting atmospheric gamma ray radiation
Predicting complex skill learning with boosting
Random forests
Summary
Chapter 8: Probabilistic Graphical Models
A little graph theory
Bayes' Theorem
Conditional independence
Bayesian networks
The Naïve Bayes classifier
Hidden Markov models
Predicting promoter gene sequences
Predicting letter patterns in English words
Summary
Chapter 9: Time Series Analysis
Fundamental concepts of time series
Some fundamental time series
Stationarity
Stationary time series models
Non-stationary time series models
Predicting intense earthquakes
Predicting lynx trappings
Predicting foreign exchange rates
Other time series models
Summary
Chapter 10: Topic Modeling
An overview of topic modeling
Latent Dirichlet Allocation
Modeling the topics of online news stories
Summary
Chapter 11: Recommendation Systems
Rating matrix
Collaborative filtering
Singular value decomposition
R and Big Data
Predicting recommendations for movies and jokes
Loading and preprocessing the data
Exploring the data
Other approaches to recommendation systems
Summary

Book Details

ISBN 139781783982806
Paperback414 pages
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