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Mastering Machine Learning with R（Second Edition）

Cory Lesmeister 著

更新時間：2021-07-09 18:24:30

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Thisbookisfordatascienceprofessionals,dataanalysts,oranyonewithaworkingknowledgeofmachinelearning,withRwhonowwanttotaketheirskillstothenextlevelandbecomeanexpertinthefield.

目錄(204章)

倒序

封面
版權信息
Credits
About the Author
About the Reviewers
Packt Upsell
Customer Feedback
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Downloading the color images of this book
Errata
Piracy
Questions
A Process for Success
The process
Business understanding
Identifying the business objective
Assessing the situation
Determining the analytical goals
Producing a project plan
Data understanding
Data preparation
Modeling
Evaluation
Deployment
Algorithm flowchart
Summary
Linear Regression - The Blocking and Tackling of Machine Learning
Univariate linear regression
Business understanding
Multivariate linear regression
Business understanding
Data understanding and preparation
Modeling and evaluation
Other linear model considerations
Qualitative features
Interaction terms
Summary
Logistic Regression and Discriminant Analysis
Classification methods and linear regression
Logistic regression
Business understanding
Data understanding and preparation
Modeling and evaluation
The logistic regression model
Logistic regression with cross-validation
Discriminant analysis overview
Discriminant analysis application
Multivariate Adaptive Regression Splines (MARS)
Model selection
Summary
Advanced Feature Selection in Linear Models
Regularization in a nutshell
Ridge regression
LASSO
Elastic net
Business case
Business understanding
Data understanding and preparation
Modeling and evaluation
Best subsets
Ridge regression
LASSO
Elastic net
Cross-validation with glmnet
Model selection
Regularization and classification
Logistic regression example
Summary
More Classification Techniques - K-Nearest Neighbors and Support Vector Machines
K-nearest neighbors
Support vector machines
Business case
Business understanding
Data understanding and preparation
Modeling and evaluation
KNN modeling
SVM modeling
Model selection
Feature selection for SVMs
Summary
Classification and Regression Trees
An overview of the techniques
Understanding the regression trees
Classification trees
Random forest
Gradient boosting
Business case
Modeling and evaluation
Regression tree
Classification tree
Random forest regression
Random forest classification
Extreme gradient boosting - classification
Model selection
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
Business understanding
Data understanding and preparation
Modeling and evaluation
An example of deep learning
H2O background
Data upload to H2O
Create train and test datasets
Modeling
Summary
Cluster Analysis
Hierarchical clustering
Distance calculations
K-means clustering
Gower and partitioning around medoids
Gower
PAM
Random forest
Business understanding
Data understanding and preparation
Modeling and evaluation
Hierarchical clustering
K-means clustering
Gower and PAM
Random Forest and PAM
Summary
Principal Components Analysis
An overview of the principal components
Rotation
Business understanding
Data understanding and preparation
Modeling and evaluation
Component extraction
Orthogonal rotation and interpretation
Creating factor scores from the components
Regression analysis
Summary
Market Basket Analysis Recommendation Engines and Sequential Analysis
An overview of a market basket analysis
Business understanding
Data understanding and preparation
Modeling and evaluation
An overview of a recommendation engine
User-based collaborative filtering
Item-based collaborative filtering
Singular value decomposition and principal components analysis
Business understanding and recommendations
Data understanding preparation and recommendations
Modeling evaluation and recommendations
Sequential data analysis
Sequential analysis applied
Summary
Creating Ensembles and Multiclass Classification
Ensembles
Business and data understanding
Modeling evaluation and selection
Multiclass classification
Business and data understanding
Model evaluation and selection
Random forest
Ridge regression
MLR's ensemble
Summary
Time Series and Causality
Univariate time series analysis
Understanding Granger causality
Business understanding
Data understanding and preparation
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 analyses
Business understanding
Data understanding and preparation
Modeling and evaluation
Word frequency and topic models
Additional quantitative analysis
Summary
R on the Cloud
Creating an Amazon Web Services account
Launch a virtual machine
Start RStudio
Summary
R Fundamentals
Getting R up-and-running
Using R
Data frames and matrices
Creating summary statistics
Installing and loading R packages
Data manipulation with dplyr
Summary
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Mastering Machine Learning with R（Second Edition）