Электронная библиотека Финансового университета

     
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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>.

Дата создания записи: 26.03.2019

Тематика: Machine learning.; R (Computer program language); Machine learning.; R (Computer program language); MATHEMATICS / Applied; MATHEMATICS / Probability & Statistics / General

Коллекции: EBSCO

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Оглавление

  • 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
  • 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
    • 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
  • 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
  • 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
  • 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
  • Chapter 11: Association Analysis
    • An overview of association analysis
      • Creating transactional data
    • Data understanding
    • Data preparation
    • Modeling and evaluation
    • Summary
  • 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
  • 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
  • Creating a Package
    • Creating a new package
    • Summary
  • Other Books You May Enjoy
  • Index

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