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Bootstrap Methods and Their Deployment in SAS and R
| Content Provider | Scilit |
|---|---|
| Author | Kolosova, Tanya Berestizhevsky, Samuel |
| Copyright Year | 2020 |
| Description | In the process of building machine learning classifiers, one of the largest challenges is to make the classifier ready for unseen data. This chapter reviews bootstrap methods and their applications and discusses how to use bootstrap methods to create multiple training and testing datasets from the original labeled data. The main reason for creating these datasets is to allow variability of data for the process of classification and, as a result, to create the classifier (or classifiers) that successfully deal with such variability. This chapter discusses how different bootstrap methods can be implemented in SAS and R. Book Name: Supervised Machine Learning |
| Related Links | https://content.taylorfrancis.com/books/download?dac=C2019-0-99832-2&isbn=9780429297595&doi=10.1201/9780429297595-4&format=pdf |
| Ending Page | 40 |
| Page Count | 12 |
| Starting Page | 29 |
| DOI | 10.1201/9780429297595-4 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2020-08-10 |
| Access Restriction | Open |
| Subject Keyword | Book Name: Supervised Machine Learning Artificial Intelligence Classification Bootstrap Methods |
| Content Type | Text |
| Resource Type | Chapter |
