Book Image

Mastering Machine Learning with R, Second Edition - Second Edition

Book Image

Mastering Machine Learning with R, Second Edition - Second Edition

Overview of this book

This book will teach you advanced techniques in machine learning with the latest code in R 3.3.2. You will delve into statistical learning theory and supervised learning; design efficient algorithms; learn about creating Recommendation Engines; use multi-class classification and deep learning; and more. You will explore, in depth, topics such as data mining, classification, clustering, regression, predictive modeling, anomaly detection, boosted trees with XGBOOST, and more. More than just knowing the outcome, you’ll understand how these concepts work and what they do. With a slow learning curve on topics such as neural networks, you will explore deep learning, and more. By the end of this book, you will be able to perform machine learning with R in the cloud using AWS in various scenarios with different datasets.

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Table of Contents (23 chapters)
Title Page
Title Page
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
Packt Upsell
Packt Upsell
Customer Feedback
Customer Feedback
Preface
Preface
Free Chapter
1
A Process for Success
A Process for Success
The process
Business understanding
Data understanding
Data preparation
Modeling
Evaluation
Deployment
Algorithm flowchart
Summary
2
Linear Regression - The Blocking and Tackling of Machine Learning
Linear Regression - The Blocking and Tackling of Machine Learning
Univariate linear regression
Multivariate linear regression
Other linear model considerations
Summary
3
Logistic Regression and Discriminant Analysis
Logistic Regression and Discriminant Analysis
Classification methods and linear regression
Logistic regression
Discriminant analysis overview
Multivariate Adaptive Regression Splines (MARS)
Model selection
Summary
4
Advanced Feature Selection in Linear Models
Advanced Feature Selection in Linear Models
Regularization in a nutshell
Business case
Modeling and evaluation
Model selection
Regularization and classification
Summary
5
More Classification Techniques - K-Nearest Neighbors and Support Vector Machines
More Classification Techniques - K-Nearest Neighbors and Support Vector Machines
K-nearest neighbors
Support vector machines
Business case
Feature selection for SVMs
Summary
6
Classification and Regression Trees
Classification and Regression Trees
An overview of the techniques
Business case
Summary
7
Neural Networks and Deep Learning
Neural Networks and Deep Learning
Introduction to neural networks
Deep learning, a not-so-deep overview
Business understanding
Data understanding and preparation
Modeling and evaluation
An example of deep learning
Summary
8
Cluster Analysis
Cluster Analysis
Hierarchical clustering
K-means clustering
Gower and partitioning around medoids
Random forest
Business understanding
Data understanding and preparation
Modeling and evaluation
Summary
9
Principal Components Analysis
Principal Components Analysis
An overview of the principal components
Business understanding
Modeling and evaluation
Summary
10
Market Basket Analysis, Recommendation Engines, and Sequential Analysis
Market Basket Analysis, Recommendation Engines, and Sequential Analysis
An overview of a market basket analysis
Business understanding
Data understanding and preparation
Modeling and evaluation
An overview of a recommendation engine
Business understanding and recommendations
Data understanding, preparation, and recommendations
Modeling, evaluation, and recommendations
Sequential data analysis
Summary
11
Creating Ensembles and Multiclass Classification
Creating Ensembles and Multiclass Classification
Ensembles
Business and data understanding
Modeling evaluation and selection
Multiclass classification
Business and data understanding
Model evaluation and selection
MLR's ensemble
Summary
12
Time Series and Causality
Time Series and Causality
Univariate time series analysis
Business understanding
Modeling and evaluation
Summary
13
Text Mining
Text Mining
Text mining framework and methods
Topic models
Business understanding
Modeling and evaluation
Summary
14
R on the Cloud
R on the Cloud
Creating an Amazon Web Services account
Summary
15
R Fundamentals
R Fundamentals
Getting R up-and-running
Using R
Data frames and matrices
Creating summary statistics
Installing and loading R packages
Data manipulation with dplyr
Summary
16
Sources
Sources

Regularization and classification

The regularization techniques applied above will also work for classification problems, both binomial and multinomial.  Therefore, let's not conclude this chapter until we apply some sample code on a logistic regression problem, specifically the breast cancer data from the prior chapter.  As in regression with a quantitative response, this can be an important technique to utilize data sets with high dimensionality.

Logistic regression example 

Recall that, in the breast cancer data we analyzed, the probability of a tumor being malignant can be denoted as follows in a logistic function:

P(malignant) = 1 / 1 + e-(B0 + B1X1 + BnXn) 

Since we have a linear component in the function, L1 and L2 regularization can be applied. To demonstrate this, let's load and prepare the breast cancer data like we did in the previous chapter:

    > library(MASS)
    > biopsy$ID = NULL
    > names(biopsy) = c("thick", "u.size", "u.shape", "adhsn",
    "s.size", "nucl", "chrom...
Previous Section
End of Section 6
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