Advanced Machine Learning With R by Lesmeister, Cory -- Read -- Imperial Library of Trantor

Index

Title Page Copyright and Credits

Advanced Machine Learning with R

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Contributors

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Preface

Who this book is for What this book covers To get the most out of this book

Download the example code files Conventions used

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Reviews

Preparing and Understanding Data

Overview Reading the data Handling duplicate observations

Descriptive statistics Exploring categorical variables

Handling missing values Zero and near-zero variance features Treating the data

Correlation and linearity

Summary

Linear Regression

Univariate linear regression

Building a univariate model Reviewing model assumptions

Multivariate linear regression

Loading and preparing the data Modeling and evaluation – stepwise regression Modeling and evaluation – MARS Reverse transformation of natural log predictions

Summary

Logistic Regression

Classification methods and linear regression Logistic regression Model training and evaluation

Training a logistic regression algorithm

Weight of evidence and information value Feature selection Cross-validation and logistic regression

Multivariate adaptive regression splines Model comparison

Summary

Advanced Feature Selection in Linear Models

Regularization overview

Ridge regression LASSO Elastic net

Data creation Modeling and evaluation

Ridge regression LASSO Elastic net

Summary

K-Nearest Neighbors and Support Vector Machines

K-nearest neighbors Support vector machines Manipulating data

Dataset creation Data preparation

Modeling and evaluation

KNN modeling Support vector machine

Summary

Tree-Based Classification

An overview of the techniques

Understanding a regression tree Classification trees Random forest Gradient boosting

Datasets and modeling

Classification tree Random forest

Extreme gradient boosting – classification

Feature selection with random forests

Summary

Neural Networks and Deep Learning

Introduction to neural networks Deep learning – a not-so-deep overview

Deep learning resources and advanced methods

Creating a simple neural network

Data understanding and preparation Modeling and evaluation

An example of deep learning

Keras and TensorFlow background Loading the data Creating the model function Model training

Summary

Creating Ensembles and Multiclass Methods

Ensembles Data understanding Modeling and evaluation

Random forest model Creating an ensemble

Summary

Cluster Analysis

Hierarchical clustering

Distance calculations

K-means clustering Gower and PAM

Gower PAM

Random forest Dataset background Data understanding and preparation Modeling

Hierarchical clustering K-means clustering Gower and PAM Random forest and PAM

Summary

Principal Component Analysis

An overview of the principal components

Rotation

Data

Data loading and review Training and testing datasets

PCA modeling

Component extraction Orthogonal rotation and interpretation Creating scores from the components Regression with MARS Test data evaluation

Summary

Association Analysis

An overview of association analysis

Creating transactional data

Data understanding Data preparation Modeling and evaluation Summary

Time Series and Causality

Univariate time series analysis

Understanding Granger causality

Time series data

Data exploration

Modeling and evaluation

Univariate time series forecasting Examining the causality

Linear regression Vector autoregression

Summary

Text Mining

Text mining framework and methods

Topic models Other quantitative analysis

Data overview

Data frame creation

Word frequency

Word frequency in all addresses Lincoln's word frequency

Sentiment analysis N-grams Topic models Classifying text

Data preparation LASSO model

Additional quantitative analysis Summary

Exploring the Machine Learning Landscape

ML versus software engineering Types of ML methods

Supervised learning Unsupervised learning Semi-supervised learning Reinforcement learning Transfer learning

ML terminology – a quick review

Deep learning Big data Natural language processing Computer vision Cost function Model accuracy Confusion matrix Predictor variables Response variable Dimensionality reduction Class imbalance problem Model bias and variance Underfitting and overfitting Data preprocessing Holdout sample Hyperparameter tuning Performance metrics Feature engineering Model interpretability

ML project pipeline

Business understanding Understanding and sourcing the data Preparing the data Model building and evaluation Model deployment

Learning paradigm Datasets Summary

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

Bagged classification and regression trees (treeBag) implementation Support vector machine bagging (SVMBag) implementation Naive Bayes (nbBag) bagging implementation

Randomization with random forests

Implementing an attrition prediction model with random forests

Boosting

The GBM implementation Building attrition prediction model with XGBoost

Stacking

Building attrition prediction model with stacking

Summary

Implementing a Jokes Recommendation Engine

Fundamental aspects of recommendation engines

Recommendation engine categories

Content-based filtering Collaborative filtering Hybrid filtering

Getting started Understanding the Jokes recommendation problem and the dataset

Converting the DataFrame Dividing the DataFrame

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

The Apriori algorithm

Content-based recommendation engine

Differentiating between ITCF and content-based recommendations

Building a hybrid recommendation system for Jokes recommendations Summary References

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

Pros and cons of 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

Customer Segmentation Using Wholesale Data

Understanding customer segmentation Understanding the wholesale customer dataset and the segmentation problem

Categories of clustering algorithms

Identifying the customer segments in wholesale customer data using k-means clustering

Working mechanics of the k-means algorithm

Identifying the customer segments in the wholesale customer data using DIANA Identifying the customer segments in the wholesale customers data using AGNES Summary

Image Recognition Using Deep Neural Networks

Technical requirements Understanding computer vision Achieving computer vision with deep learning

Convolutional Neural Networks

Layers of CNNs

Introduction to the MXNet framework Understanding the MNIST dataset Implementing a deep learning network for handwritten digit recognition

Implementing dropout to avoid overfitting Implementing the LeNet architecture with the MXNet library

Implementing computer vision with pretrained models Summary

Credit Card Fraud Detection Using Autoencoders

Machine learning in credit card fraud detection Autoencoders explained

Types of AEs based on hidden layers Types of AEs based on restrictions Applications of AEs

The credit card fraud dataset Building AEs with the H2O library in R

Autoencoder code implementation for credit card fraud detection

Summary

Automatic Prose Generation with Recurrent Neural Networks

Understanding language models Exploring recurrent neural networks

Comparison of feedforward neural networks and RNNs

Backpropagation through time Problems and solutions to gradients in RNN

Exploding gradients Vanishing gradients

Building an automated prose generator with an RNN

Implementing the project

Summary

Winning the Casino Slot Machines with Reinforcement Learning

Understanding RL

Comparison of RL with other ML algorithms Terminology of RL The multi-arm bandit problem Strategies for solving MABP

The epsilon-greedy algorithm Boltzmann or softmax exploration Decayed epsilon greedy The upper confidence bound algorithm Thompson sampling

Multi-arm bandit – real-world use cases Solving the MABP with UCB and Thompson sampling algorithms Summary

Creating a Package

Creating a new package Summary

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