7. Ensemble Methods | Mastering Predictive Analytics with R

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Table Of Contents

Mastering Predictive Analytics with R

3.9 (18)

Mastering Predictive Analytics with R

3.9 (18)

Overview of this book

This book is intended for the budding data scientist, predictive modeler, or quantitative analyst with only a basic exposure to R and statistics. It is also designed to be a reference for experienced professionals wanting to brush up on the details of a particular type of predictive model. Mastering Predictive Analytics with R assumes familiarity with only the fundamentals of R, such as the main data types, simple functions, and how to move data around. No prior experience with machine learning or predictive modeling is assumed, however you should have a basic understanding of statistics and calculus at a high school level.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. Gearing Up for Predictive Modeling

1. Gearing Up for Predictive Modeling

Models

Types of models

The process of predictive modeling

Performance metrics

Summary

2. Linear Regression

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

3. Logistic Regression

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

4. Neural Networks

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

5. Support Vector Machines

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

6. Tree-based Methods

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

7. Ensemble Methods

7. Ensemble Methods

Bagging

Boosting

Predicting atmospheric gamma ray radiation

Predicting complex skill learning with boosting

Random forests

Summary

8. Probabilistic Graphical Models

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

9. Time Series Analysis

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

10. Topic Modeling

10. Topic Modeling

An overview of topic modeling

Latent Dirichlet Allocation

Modeling the topics of online news stories

Summary

11. Recommendation Systems

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

Index

Index

Bagging

The focus of this chapter is on combining the results from different models in order to produce a single model that will outperform individual models on their own. Bagging is essentially an intuitive procedure for combining multiple models trained on the same data set, by using majority voting for classification models and average value for regression models. We'll present this procedure for the classification case, and later show how this is easily extended to handle regression models.

Bagging procedure for binary classification

Inputs:

data: The input data frame containing the input features and a column with the binary output label
M: An integer, representing the number of models that we want to train

Output:

models: A set of Μ trained binary classifier models

Method:

1. Create a random sample of size n, where n is the number of observations in the original data set, with replacement. This means that some of the observations from the original training set will be repeated and some...

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