MARC 닫기

• LDR

  05491cam a2200577Ii 4500

• 001

  000000533176

• 005

  20210114162029

• 006

  m d

• 007

  cr unu||||||||

• 008

  190509s2019 enka o 000 0 eng d

• 015

  ▼a  GBB995017
  ▼2  bnb

• 016

  ▼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
  ▼e  pn
  ▼c  UMI
  ▼d  TEFOD
  ▼d  EBLCP
  ▼d  MERUC
  ▼d  UKMGB
  ▼d  OCLCF
  ▼d  YDX
  ▼d  UKAHL
  ▼d  OCLCQ
  ▼d  N$T
  ▼d  248023

• 050

  ▼a  Q325.5

• 082

  ▼a  006.31
  ▼2  23

• 100

  ▼a  Pastor Sanz, Iván,
  ▼e  author.

• 245

  ▼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  Iván 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

  ▼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

  ▼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

  ▼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

  ▼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

  ▼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

  ▼a  Online resource; title from title page (Safari, viewed May 9, 2019).

• 590

  ▼a  Added to collection customer.56279.3

• 650

  ▼a  Machine learning.

• 650

  ▼a  R (Computer program language)

• 650

  ▼a  Machine learning.
  ▼2  fast
  ▼0  (OCoLC)fst01004795

• 650

  ▼a  R (Computer program language)
  ▼2  fast
  ▼0  (OCoLC)fst01086207

• 655

  ▼a  Electronic books.

• 776

  ▼i  Print version:
  ▼a  Sanz, Iván 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, ©2019,
  ▼z  9781838644338

• 856

  ▼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  강리원

• 991

  ▼a  eBook

• 994

  ▼a  92
  ▼b  N$T