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| 008 | | 190509s2019 enka o 000 0 eng d |
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▼a 1091700087
▼a 1096538373 |
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▼a 9781838647056
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▼z 9781838644338 |
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▼a 2094784
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▼a 006.31
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| 100 | 1 |
▼a Pastor Sanz, Iva?n,
▼e author. |
| 245 | 10 |
▼a Machine learning with R quick start guide :
▼b a beginner's guide to implementing machine learning techniques from scratch using R 3.5 /
▼c Iva?n Pastor Sanz. |
| 260 | |
▼a Birmingham, UK :
▼b Packt Publishing,
▼c 2019. |
| 300 | |
▼a 1 online resource :
▼b illustrations |
| 336 | |
▼a text
▼b txt
▼2 rdacontent |
| 337 | |
▼a computer
▼b c
▼2 rdamedia |
| 338 | |
▼a online resource
▼b cr
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| 505 | 0 |
▼a 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 |
| 505 | 8 |
▼a 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 |
| 505 | 8 |
▼a 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 |
| 505 | 8 |
▼a 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 |
| 505 | 8 |
▼a 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 |
| 520 | |
▼a 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 cases. |
| 588 | 0 |
▼a Online resource; title from title page (Safari, viewed May 9, 2019). |
| 590 | |
▼a Added to collection customer.56279.3 |
| 650 | 0 |
▼a Machine learning. |
| 650 | 0 |
▼a R (Computer program language) |
| 650 | 7 |
▼a Machine learning.
▼2 fast
▼0 (OCoLC)fst01004795 |
| 650 | 7 |
▼a R (Computer program language)
▼2 fast
▼0 (OCoLC)fst01086207 |
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▼a Electronic books. |
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▼i Print version:
▼a Sanz, Iva?n Pastor.
▼t Machine Learning with R Quick Start Guide : A Beginner's Guide to Implementing Machine Learning Techniques from Scratch Using R 3. 5.
▼d Birmingham : Packt Publishing Ltd, 짤2019
▼z 9781838644338 |
| 856 | 40 |
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