Advanced Machine Learning with R电子书

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Preface

Who this book is for

What this book covers

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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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