LDR | | 05491cmm u2200577Ii 4500 |
001 | | 000000316191 |
003 | | OCoLC |
005 | | 20230525175942 |
006 | | m d |
007 | | cr unu|||||||| |
008 | | 190509s2019 enka o 000 0 eng d |
015 | |
▼a GBB995017
▼2 bnb |
016 | 7 |
▼a 019365493
▼2 Uk |
019 | |
▼a 1091700087
▼a 1096538373 |
020 | |
▼a 1838647058 |
020 | |
▼a 9781838647056
▼q (electronic bk.) |
020 | |
▼z 9781838644338 |
035 | |
▼a 2094784
▼b (N$T) |
035 | |
▼a (OCoLC)1100643399
▼z (OCoLC)1091700087
▼z (OCoLC)1096538373 |
037 | |
▼a CL0501000047
▼b Safari Books Online |
040 | |
▼a UMI
▼b eng
▼e rda
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▼c UMI
▼d TEFOD
▼d EBLCP
▼d MERUC
▼d UKMGB
▼d OCLCF
▼d YDX
▼d UKAHL
▼d OCLCQ
▼d N$T
▼d 248032 |
049 | |
▼a MAIN |
050 | 4 |
▼a Q325.5 |
082 | 04 |
▼a 006.31
▼2 23 |
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
▼2 rdacarrier |
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 |
655 | 4 |
▼a Electronic books. |
776 | 08 |
▼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 |
▼3 EBSCOhost
▼u http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2094784 |
938 | |
▼a Askews and Holts Library Services
▼b ASKH
▼n BDZ0039952970 |
938 | |
▼a ProQuest Ebook Central
▼b EBLB
▼n EBL5744469 |
938 | |
▼a YBP Library Services
▼b YANK
▼n 16142490 |
938 | |
▼a EBSCOhost
▼b EBSC
▼n 2094784 |
990 | |
▼a 관리자 |
994 | |
▼a 92
▼b N$T |