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Design of Experiments and Its Deployment in SAS and R
| Content Provider | Scilit |
|---|---|
| Author | Kolosova, Tanya Berestizhevsky, Samuel |
| Copyright Year | 2020 |
| Description | This chapter reviews the design of experiment methodology and covers its application to find a combination of conditions that deliver the best classification results. The chapter demonstrates how the design of experiment statistical methodology is used as a framework for optimizing values of hyperparameters, with the objective to find the best setup for a classifier. Analysis of the experimental results is performed by the analysis of variance methodology and by estimating a linear mixed model. The chapter discusses and reviews the functions available in SAS and R to design an experiment and to analyze its results. Book Name: Supervised Machine Learning |
| Related Links | https://content.taylorfrancis.com/books/download?dac=C2019-0-99832-2&isbn=9780429297595&doi=10.1201/9780429297595-6&format=pdf |
| Ending Page | 67 |
| Page Count | 21 |
| Starting Page | 47 |
| DOI | 10.1201/9780429297595-6 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2020-09-21 |
| Access Restriction | Open |
| Subject Keyword | Book Name: Supervised Machine Learning Experimental Hyperparameters Classification |
| Content Type | Text |
| Resource Type | Chapter |