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Lesmeister, Cory. Mastering machine learning with R: advanced machine learning techniques for building smart applications with R 3.5 / Cory Lesmeister. — Third edition. — 1 online resource (1 volume) : illustrations. — Previous edition published: 2017. — <URL:http://elib.fa.ru/ebsco/2016363.pdf>.Record create date: 3/26/2019 Subject: Machine learning.; R (Computer program language); Machine learning.; R (Computer program language); MATHEMATICS / Applied; MATHEMATICS / Probability & Statistics / General Collections: EBSCO Allowed Actions: –
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Table of Contents
- Cover
- Title Page
- Copyright and Credits
- About Packt
- Contributors
- Table of Contents
- Preface
- Chapter 1: 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
- Chapter 2: 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
- Univariate linear regression
- Chapter 3: 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
- Training a logistic regression algorithm
- Summary
- Chapter 4: Advanced Feature Selection in Linear Models
- Regularization overview
- Ridge regression
- LASSO
- Elastic net
- Data creation
- Modeling and evaluation
- Ridge regression
- LASSO
- Elastic net
- Summary
- Regularization overview
- Chapter 5: 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
- Chapter 6: 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
- An overview of the techniques
- Chapter 7: 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
- Chapter 8: Creating Ensembles and Multiclass Methods
- Ensembles
- Data understanding
- Modeling and evaluation
- Random forest model
- Creating an ensemble
- Summary
- Chapter 9: 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
- Hierarchical clustering
- Chapter 10: 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
- An overview of the principal components
- Chapter 11: Association Analysis
- An overview of association analysis
- Creating transactional data
- Data understanding
- Data preparation
- Modeling and evaluation
- Summary
- An overview of association analysis
- Chapter 12: 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
- Univariate time series analysis
- Chapter 13: 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
- Text mining framework and methods
- Creating a Package
- Creating a new package
- Summary
- Other Books You May Enjoy
- Index
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