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by Ivan Pastor Sanz
Machine Learning with R Quick Start Guide
Title Page
Copyright and Credits
Machine Learning with R Quick Start Guide
About Packt
Why subscribe?
Packt.com
Contributors
About the author
About the reviewer
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
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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