更新时间：2021-06-24 14:25:35
coverpage
Title Page
Copyright and Credits
Advanced Machine Learning with R
About Packt
Why subscribe?
Packt.com
Contributors
About the authors
Packt is searching for authors like you
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
Get in touch
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
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
Advanced Feature Selection in Linear Models
Regularization overview
Ridge regression
LASSO
Elastic net
Data creation
Modeling and evaluation
K-Nearest Neighbors and Support Vector Machines
K-nearest neighbors
Support vector machines
Manipulating data
Dataset creation
Data preparation
KNN modeling
Support vector machine
Tree-Based Classification
An overview of the techniques
Understanding a regression tree
Classification trees
Random forest
Gradient boosting
Datasets and modeling
Classification tree
Extreme gradient boosting – classification
Feature selection with random forests
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
An example of deep learning
Keras and TensorFlow background
Loading the data
Creating the model function
Model training
Creating Ensembles and Multiclass Methods
Ensembles
Data understanding
