Mastering Machine Learning with R电子书

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Mastering Machine Learning with R Third Edition

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Preface

Who this book is for

What this book covers

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Conventions used

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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

Creating a Package

Creating a new package

Summary

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