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Mastering Predictive Analytics with R

By : Rui Miguel Forte, Rui Miguel Forte
Book Image

Mastering Predictive Analytics with R

By: Rui Miguel Forte, Rui Miguel Forte

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Mastering Predictive Analytics with R
Rui Miguel Forte | Rui Miguel Forte
Jun, 2015 | 414 pages
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Table of Contents (19 chapters)
Mastering Predictive Analytics with R
Mastering Predictive Analytics with R
Credits
Credits
About the Author
About the Author
Acknowledgments
Acknowledgments
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Gearing Up for Predictive Modeling
Gearing Up for Predictive Modeling
Models
Types of models
The process of predictive modeling
Performance metrics
Summary
2
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
Summary
3
Logistic Regression
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
4
Neural Networks
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
5
Support Vector Machines
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
6
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
Summary
7
Ensemble Methods
Ensemble Methods
Bagging
Boosting
Predicting atmospheric gamma ray radiation
Predicting complex skill learning with boosting
Random forests
Summary
8
Probabilistic Graphical Models
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
9
Time Series Analysis
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
10
Topic Modeling
Topic Modeling
An overview of topic modeling
Latent Dirichlet Allocation
Modeling the topics of online news stories
Summary
11
Recommendation Systems
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
Index
Index

Chapter 2. Linear Regression

We learned from the previous chapter that regression problems involve predicting a numerical output. The simplest but most common type of regression is linear regression. In this chapter, we'll explore why linear regression is so commonly used, its limitations, and extensions.

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End of Section 1
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