book

Mastering Predictive Analytics with R

Name: Mastering Predictive Analytics with R
Author: Rui Miguel Forte
ISBN: 9781783982806

by Rui Miguel Forte

June 2015

Beginner to intermediate

414 pages

11h 26m

English

Packt Publishing

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Mastering Predictive Analytics with R
Table of Contents
Mastering Predictive Analytics with R
Credits
About the Author
Acknowledgments
About the Reviewers
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Preface
What this book covers
What you need for this book

Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example codeErrataPiracyQuestions
1. Gearing Up for Predictive Modeling
ModelsLearning from dataThe core components of a modelOur first model: k-nearest neighbors
Types of models
Supervised, unsupervised, semi-supervised, and reinforcement learning modelsParametric and nonparametric modelsRegression and classification modelsReal-time and batch machine learning models
The process of predictive modeling
Defining the model's objectiveCollecting the dataPicking a modelPreprocessing the dataExploratory data analysisFeature transformationsEncoding categorical featuresMissing dataOutliersRemoving problematic featuresFeature engineering and dimensionality reductionTraining and assessing the modelRepeating with different models and final model selectionDeploying the model
Performance metrics
Assessing regression modelsAssessing classification modelsAssessing binary classification models
Summary
2. Linear Regression
Introduction to linear regressionAssumptions of linear regression
Simple linear regression
Estimating the regression coefficients
Multiple linear regression
Predicting CPU performancePredicting the price of used cars
Assessing linear regression models
Residual analysisSignificance tests for linear regressionPerformance metrics for linear regressionComparing different regression modelsTest set performance
Problems with linear regression
MulticollinearityOutliers
Feature selection
Regularization
Ridge regressionLeast absolute shrinkage and selection operator (lasso)Implementing regularization in R
Summary
3. Logistic Regression
Classifying with linear regression
Introduction to logistic regression
Generalized linear modelsInterpreting coefficients in logistic regressionAssumptions of logistic regressionMaximum likelihood estimation
Predicting heart disease
Assessing logistic regression models
Model devianceTest set performance
Regularization with the lasso
Classification metrics
Extensions of the binary logistic classifier
Multinomial logistic regressionPredicting glass typeOrdinal logistic regressionPredicting wine quality
Summary
4. Neural Networks
The biological neuron
The artificial neuron
Stochastic gradient descent
Gradient descent and local minimaThe perceptron algorithmLinear separationThe logistic neuron
Multilayer perceptron networks
Training multilayer perceptron networks
Predicting the energy efficiency of buildings
Evaluating multilayer perceptrons for regression
Predicting glass type revisited
Predicting handwritten digits
Receiver operating characteristic curves
Summary
5. Support Vector Machines
Maximal margin classification
Support vector classification
Inner products
Kernels and support vector machines
Predicting chemical biodegration
Cross-validation
Predicting credit scores
Multiclass classification with support vector machines
Summary
6. Tree-based Methods
The intuition for tree models
Algorithms for training decision trees
Classification and regression treesCART regression treesTree pruningMissing dataRegression model treesCART classification treesC5.0
Predicting class membership on synthetic 2D data
Predicting the authenticity of banknotes
Predicting complex skill learning
Tuning model parameters in CART treesVariable importance in tree modelsRegression model trees in action
Summary
7. Ensemble Methods
BaggingMargins and out-of-bag observationsPredicting complex skill learning with baggingPredicting heart disease with baggingLimitations of bagging
Boosting
AdaBoost
Predicting atmospheric gamma ray radiation
Predicting complex skill learning with boosting
Limitations of boosting
Random forests
The importance of variables in random forests
Summary
8. Probabilistic Graphical Models
A little graph theory
Bayes' Theorem
Conditional independence
Bayesian networks
The Naïve Bayes classifier
Predicting the sentiment of movie reviews
Hidden Markov models
Predicting promoter gene sequences
Predicting letter patterns in English words
Summary
9. Time Series Analysis
Fundamental concepts of time seriesTime series summary functions
Some fundamental time series
White noiseFitting a white noise time seriesRandom walkFitting a random walk
Stationarity
Stationary time series models
Moving average modelsAutoregressive modelsAutoregressive moving average models
Non-stationary time series models
Autoregressive integrated moving average modelsAutoregressive conditional heteroscedasticity modelsGeneralized autoregressive heteroscedasticity models
Predicting intense earthquakes
Predicting lynx trappings
Predicting foreign exchange rates
Other time series models
Summary
10. Topic Modeling
An overview of topic modeling
Latent Dirichlet Allocation
The Dirichlet distributionThe generative processFitting an LDA model
Modeling the topics of online news stories
Model stabilityFinding the number of topicsTopic distributionsWord distributionsLDA extensions
Summary
11. Recommendation Systems
Rating matrixMeasuring user similarity
Collaborative filtering
User-based collaborative filteringItem-based collaborative filtering
Singular value decomposition
R and Big Data
Predicting recommendations for movies and jokes
Loading and preprocessing the data
Exploring the data
Evaluating binary top-N recommendationsEvaluating non-binary top-N recommendationsEvaluating individual predictions
Other approaches to recommendation systems
Summary
Index

Content preview from Mastering Predictive Analytics with R

Problems with linear regression

In this chapter, we've already seen some examples where trying to build a linear regression model might run into problems. One big class of problems that we've talked about is related to our model assumptions of linearity, feature independence, and the homoscedasticity and normality of errors. In particular we saw methods of diagnosing these problems either via plots, such as the residual plot, or by using functions that identify dependent components. In this section, we'll investigate a few more issues that can arise with linear regression.

Multicollinearity

As part of our preprocessing steps, we were diligent to remove features that were linearly related to each other. In doing this we were looking for an exact ...

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Mastering Predictive Analytics with R

by Rui Miguel Forte

Problems with linear regression

Multicollinearity

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