Machine Learning with R Quick Start Guide电子书

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Preface

Who this book is for

What this book covers

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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 list

Data 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

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 data

Summary

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

Predicting Failures of Banks - Univariate Analysis

Feature selection algorithm

Feature 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

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 parameters

The 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

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

Acquiring the target variable

Acquiring the credit quality

Displaying the credit ratings on a map

Carrying out a descriptive analysis of data

Detecting macroeconomic imbalances

The self-organizing maps technique

Training the SOM

Summary

Sovereign Crisis - NLP and Topic Modeling

Predicting country ratings using macroeconomic information

Implementing decision trees

Ordered logistic regression

Predicting sovereign ratings using European country reports

Summary

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