Book Image

Advanced Machine Learning with R

By : Cory Lesmeister, Dr. Sunil Kumar Chinnamgari
Book Image

Advanced Machine Learning with R

By: Cory Lesmeister, Dr. Sunil Kumar Chinnamgari

Overview of this book

R is one of the most popular languages when it comes to exploring the mathematical side of machine learning and easily performing computational statistics. This Learning Path shows you how to leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization. You’ll work through realistic projects such as building powerful machine learning models with ensembles to predict employee attrition. Next, you’ll explore different clustering techniques to segment customers using wholesale data and even apply TensorFlow and Keras-R for performing advanced computations. Each chapter will help you implement advanced machine learning algorithms using real-world examples. You’ll also be introduced to reinforcement learning along with its use cases and models. Finally, this Learning Path will provide you with a glimpse into how some of these black box models can be diagnosed and understood. By the end of this Learning Path, you’ll be equipped with the skills you need to deploy machine learning techniques in your own projects.

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Advanced Machine Learning with R
Cory Lesmeister | Dr. Sunil Kumar Chinnamgari
May, 2019 | 11 hours 4 minutes
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Table of Contents (30 chapters)
Title Page
Title Page
Copyright and Credits
Copyright and Credits
About Packt
About Packt
Contributors
Contributors
Preface
Preface
Free Chapter
1
Preparing and Understanding Data
Preparing and Understanding Data
Overview
Reading the data
Handling duplicate observations
Handling missing values
Zero and near-zero variance features
Treating the data
Summary
2
Linear Regression
Linear Regression
Univariate linear regression
Multivariate linear regression
Summary
3
Logistic Regression
Logistic Regression
Classification methods and linear regression
Logistic regression
Model training and evaluation
Summary
4
Advanced Feature Selection in Linear Models
Advanced Feature Selection in Linear Models
Regularization overview
Data creation
Modeling and evaluation
Summary
5
K-Nearest Neighbors and Support Vector Machines
K-Nearest Neighbors and Support Vector Machines
K-nearest neighbors
Support vector machines
Manipulating data
Modeling and evaluation
Summary
6
Tree-Based Classification
Tree-Based Classification
An overview of the techniques
Datasets and modeling
Summary
7
Neural Networks and Deep Learning
Neural Networks and Deep Learning
Introduction to neural networks
Deep learning – a not-so-deep overview
Creating a simple neural network
An example of deep learning
Summary
8
Creating Ensembles and Multiclass Methods
Creating Ensembles and Multiclass Methods
Ensembles
Data understanding
Modeling and evaluation
Summary
9
Cluster Analysis
Cluster Analysis
Hierarchical clustering
K-means clustering
Gower and PAM
Random forest
Dataset background
Data understanding and preparation
Modeling 
Summary
10
Principal Component Analysis
Principal Component Analysis
An overview of the principal components
Data
PCA modeling
Summary
11
Association Analysis
Association Analysis
An overview of association analysis
Data understanding
Data preparation
Modeling and evaluation
Summary
12
Time Series and Causality
Time Series and Causality
Univariate time series analysis
Time series data
Modeling and evaluation
Summary
13
Text Mining
Text Mining
Text mining framework and methods
Data overview
Word frequency
Sentiment analysis
N-grams
Topic models
Classifying text
Additional quantitative analysis
Summary
14
Exploring the Machine Learning Landscape
Exploring the Machine Learning Landscape
ML versus software engineering
Types of ML methods
ML terminology – a quick review
ML project pipeline
Learning paradigm
Datasets
Summary
15
Predicting Employee Attrition Using Ensemble Models
Predicting Employee Attrition Using Ensemble Models
Philosophy behind ensembling 
Getting started
Understanding the attrition problem and the dataset 
K-nearest neighbors model for benchmarking the performance
Bagging
Randomization with random forests
Boosting 
Stacking 
Summary
16
Implementing a Jokes Recommendation Engine
Implementing a Jokes Recommendation Engine
Fundamental aspects of recommendation engines
Getting started
Understanding the Jokes recommendation problem and the dataset
Building a recommendation system with an item-based collaborative filtering technique
Building a recommendation system with a user-based collaborative filtering technique
Building a recommendation system based on an association-rule mining technique
Content-based recommendation engine
Building a hybrid recommendation system for Jokes recommendations
Summary
References
17
Sentiment Analysis of Amazon Reviews with NLP
Sentiment Analysis of Amazon Reviews with NLP
The sentiment analysis problem
Getting started
Understanding the Amazon reviews dataset
Building a text sentiment classifier with the BoW approach
Understanding word embedding
Building a text sentiment classifier with pretrained word2vec word embedding based on Reuters news corpus
Building a text sentiment classifier with GloVe word embedding
Building a text sentiment classifier with fastText
Summary
18
Customer Segmentation Using Wholesale Data
Customer Segmentation Using Wholesale Data
Understanding customer segmentation
Understanding the wholesale customer dataset and the segmentation problem
Identifying the customer segments in wholesale customer data using k-means clustering
Identifying the customer segments in the wholesale customer data using DIANA
Identifying the customer segments in the wholesale customers data using AGNES
Summary
19
Image Recognition Using Deep Neural Networks
Image Recognition Using Deep Neural Networks
Technical requirements
Understanding computer vision
Achieving computer vision with deep learning
Introduction to the MXNet framework
Understanding the MNIST dataset
Implementing a deep learning network for handwritten digit recognition
Implementing computer vision with pretrained models
Summary
20
Credit Card Fraud Detection Using Autoencoders
Credit Card Fraud Detection Using Autoencoders
Machine learning in credit card fraud detection
Autoencoders explained
The credit card fraud dataset
Building AEs with the H2O library in R
Summary
21
Automatic Prose Generation with Recurrent Neural Networks
Automatic Prose Generation with Recurrent Neural Networks
Understanding language models
Exploring recurrent neural networks
Backpropagation through time
Problems and solutions to gradients in RNN
Building an automated prose generator with an RNN
Summary
22
Winning the Casino Slot Machines with Reinforcement Learning
Winning the Casino Slot Machines with Reinforcement Learning
Understanding RL
Multi-arm bandit – real-world use cases
Solving the MABP with UCB and Thompson sampling algorithms
Summary
Creating a Package
Creating a Package
Creating a new package
Summary
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Index
Index

Randomization with random forests

As we've seen in bagging, we create a number of bags on which each model is trained. Each of the bags consists of subsets of the actual dataset, however the number of features or variables remain the same in each of the bags. In other words, what we performed in bagging is subsetting the dataset rows. 

In random forests, while we create bags from the dataset through subsetting the rows, we also subset the features (columns) that need to be included in each of the bags.

Assume that you have 1,000 observations with 20 features in your dataset. We can create 20 bags where each one of the bags has 100 observations (this is possible because of bootstrapping with replacement) and five features. Now 20 models are trained where each model gets to see only the bag it is assigned with. The final prediction is arrived at by voting or averaging based on the fact of whether the problem is a regression problem or a classification problem.

Another key difference between bagging...

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