Machine learning with R quick start guide : a beginner's guide to implementing machine learning techniques from scratch using R 3.5 /

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This book is ideal for people wanting to get up-and-running with the core concepts of machine learning using R 3.5. This book follows a step-by-step approach to implementing an end-to-end pipeline, addressing data collection and processing, various types of data analysis, and machine learning use ca...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Pastor Sanz, Iván (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham, UK : Packt Publishing, 2019.
Temas:
Machine learning.
R (Computer program language)
Apprentissage automatique.
R (Langage de programmation)
Machine learning
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: R Fundamentals for Machine Learning; R and RStudio installation; Things to know about R; Using RStudio; RStudio installation ; Some basic commands; Objects, special cases, and basic operators in R; Working with objects; Working with vectors; Vector indexing; Functions on vectors; Factor; Factor levels; Strings; String functions; Matrices; Representing matrices; Creating matrices; Accessing elements in a matrix; Matrix functions; Lists; Creating lists
  • Accessing components and elements in a listData frames; Accessing elements in data frames; Functions of data frames; Importing or exporting data; Working with functions; Controlling code flow; All about R packages; Installing packages; Necessary packages; Taking further steps; Background on the financial crisis; Summary; Chapter 2: Predicting Failures of Banks
  • Data Collection; Collecting financial data; Why FDIC?; Listing files; Finding files; Combining results; Removing tables; Knowing your observations; Handling duplications; Operating our problem; Collecting the target variable
  • Structuring dataSummary; Chapter 3: Predicting Failures of Banks
  • Descriptive Analysis; Data overview; Getting acquainted with our variables; Finding missing values for a variable; Converting the format of the variables; Sampling; Partitioning samples; Checking samples; Implementing descriptive analysis; Dealing with outliers; The winsorization process; Implementing winsorization; Distinguishing single valued variables; Treating missing information; Analyzing the missing value; Understanding the results; Summary; Chapter 4: Predicting Failures of Banks
  • Univariate Analysis
  • Feature selection algorithmFeature selection classes; Filter methods; Wrapper methods; Boruta package; Embedded methods; Ridge regression; A limitation of Ridge regression; Lasso ; Limitations of Lasso; Elastic net; Drawbacks of elastic net; Dimensionality reduction; Dimensionality reduction technique; Summary; Chapter 5: Predicting Failures of Banks
  • Multivariate Analysis; Logistic regression; Regularized methods; Testing a random forest model; Gradient boosting; Deep learning in neural networks; Designing a neural network; Training a neural network; Support vector machines
  • Selecting SVM parametersThe SVM kernel parameter; The cost parameter; Gamma parameter; Training an SVM model; Ensembles; Average model; Majority vote; Model of models; Automatic machine learning; Standardizing variables; Summary ; Chapter 6: Visualizing Economic Problems in the European Union; A general overview of economic problems in countries; Understanding credit ratings; The role of credit rating agencies; The credit rating process; Clustering countries based on macroeconomic imbalances; Data collection; Downloading and viewing the data; Streamlining data; Studying the data

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