Supervised Machine Learning : Optimization Framework and Applications with SAS and R/ Tanya Kolosova, Samuel Berestizhevsky
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TextPublication details: New York : CRC Press , 2021.Edition: 1st edDescription: xx, 160 p. : ill. ; 23 cmISBN: - 9780367538828
- 006.31 KOS
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KU Central Library | Rack No. : 01 Annex : 01 Shelve No. : A-04 | Reference Section (Non-Issuable Books) | 006.31 KOS 2021 (Browse shelf(Opens below)) | C-2 (NI) | Not For Loan | 53616 |
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| 005.55 GER 2021 R Visualizations : derive meaning from data / | 005.55 GER 2021 R Visualizations : derive meaning from data / | 005.55 GER 2021 R Visualizations : derive meaning from data / | 006.31 KOS 2021 Supervised Machine Learning : Optimization Framework and Applications with SAS and R/ | 006.31 KOS 2021 Supervised Machine Learning : Optimization Framework and Applications with SAS and R/ | 006.31 KOS 2021 Supervised Machine Learning : Optimization Framework and Applications with SAS and R/ | 006.696 ULF 2005 Flash 5 for Windows and Macintosh / |
Includes bibliographical references and index.
Introduction. PART 1 1.Introduction to the AI framework.
2.Supervised Machine Learning and Its Deployment in SAS and R.
3.Bootstrap methods and Its Deployment in SAS and R.
4.Outliers Detection and Its Deployment in SAS and R.
5.Design of Experiment and Its Deployment in SAS and R.
PART II 1.Introduction to the SAS and R based table-driven environment.
2.Input Data component.
3.Design of Experiment for Machine-Learning component.
4.“Contaminated” Training Datasets Component.
PART III 1.Insurance Industry: Underwriters decision-making process.
2.Insurance Industry: Claims Modeling and Prediction.
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