Machine learning with R quick start guide :: a beginner's guide to implementing machine learning techniques from scratch using R 3.5 /
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 ca...
Gespeichert in:
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham, UK :
Packt Publishing,
2019.
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| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | 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. |
| Beschreibung: | 1 online resource : illustrations |
| ISBN: | 1838647058 9781838647056 |
Internformat
MARC
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| 245 | 1 | 0 | |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. |
| 264 | 1 | |a Birmingham, UK : |b Packt Publishing, |c 2019. | |
| 300 | |a 1 online resource : |b illustrations | ||
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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. | ||
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| 650 | 7 | |a R (Computer program language) |2 fast | |
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| 776 | 0 | 8 | |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 |
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| contents | 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 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 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 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 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 |
| ctrlnum | (OCoLC)1100643399 |
| dewey-full | 006.31 |
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| dewey-ones | 006 - Special computer methods |
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| discipline | Informatik |
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| language | English |
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| spelling | Pastor Sanz, Iván, author. Machine learning with R quick start guide : a beginner's guide to implementing machine learning techniques from scratch using R 3.5 / Iván Pastor Sanz. Birmingham, UK : Packt Publishing, 2019. 1 online resource : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Online resource; title from title page (Safari, viewed May 9, 2019). 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 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 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 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 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 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. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 R (Computer program language) http://id.loc.gov/authorities/subjects/sh2002004407 Apprentissage automatique. R (Langage de programmation) Machine learning fast R (Computer program language) fast Electronic book. has work: Machine learning with R quick start guide (Text) https://id.oclc.org/worldcat/entity/E39PD3HwPYVjrpDJ7HP8xp3cw3 https://id.oclc.org/worldcat/ontology/hasWork Print version: Sanz, Iván Pastor. Machine Learning with R Quick Start Guide : A Beginner's Guide to Implementing Machine Learning Techniques from Scratch Using R 3. 5. Birmingham : Packt Publishing Ltd, ©2019 9781838644338 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2094784 Volltext |
| spellingShingle | Pastor Sanz, Iván Machine learning with R quick start guide : a beginner's guide to implementing machine learning techniques from scratch using R 3.5 / 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 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 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 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 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 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 R (Computer program language) http://id.loc.gov/authorities/subjects/sh2002004407 Apprentissage automatique. R (Langage de programmation) Machine learning fast R (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh2002004407 |
| title | Machine learning with R quick start guide : a beginner's guide to implementing machine learning techniques from scratch using R 3.5 / |
| title_auth | Machine learning with R quick start guide : a beginner's guide to implementing machine learning techniques from scratch using R 3.5 / |
| title_exact_search | Machine learning with R quick start guide : a beginner's guide to implementing machine learning techniques from scratch using R 3.5 / |
| title_full | Machine learning with R quick start guide : a beginner's guide to implementing machine learning techniques from scratch using R 3.5 / Iván Pastor Sanz. |
| title_fullStr | Machine learning with R quick start guide : a beginner's guide to implementing machine learning techniques from scratch using R 3.5 / Iván Pastor Sanz. |
| title_full_unstemmed | Machine learning with R quick start guide : a beginner's guide to implementing machine learning techniques from scratch using R 3.5 / Iván Pastor Sanz. |
| title_short | Machine learning with R quick start guide : |
| title_sort | machine learning with r quick start guide a beginner s guide to implementing machine learning techniques from scratch using r 3 5 |
| title_sub | a beginner's guide to implementing machine learning techniques from scratch using R 3.5 / |
| topic | Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 R (Computer program language) http://id.loc.gov/authorities/subjects/sh2002004407 Apprentissage automatique. R (Langage de programmation) Machine learning fast R (Computer program language) fast |
| topic_facet | Machine learning. R (Computer program language) Apprentissage automatique. R (Langage de programmation) Machine learning Electronic book. |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2094784 |
| work_keys_str_mv | AT pastorsanzivan machinelearningwithrquickstartguideabeginnersguidetoimplementingmachinelearningtechniquesfromscratchusingr35 |