Читать книгу Fundamentals and Methods of Machine and Deep Learning - Pradeep Singh - Страница 43
2.8 Efficiency Analysis
ОглавлениеThe efficiency achieved by the considered ensemble machine learning techniques, i.e., Bayes optimal classifier, bagging, boosting, BMA, bucket of models, and tacking, is compared toward the performance metrics, i.e., accuracy, throughput, execution time, response time, error rate, and learning rate [30]. From the analysis, it is observed that the efficiency achieved by Bayesian model combination, stacking, and Bayesian model combination are high compared to other ensemble models considered for identification of zonotic diseases.
| Technique | Accuracy | Throughput | Execution time | Response time | Error rate | Learning rate |
| Bayes optimal classifier | Low | Low | High | Medium | Medium | Low |
| Bagging | Low | Medium | Medium | High | Low | Low |
| Boosting | Low | Medium | High | High | High | Low |
| Bayesian model averaging | High | High | Medium | Medium | Low | Low |
| Bayesian model combination | High | High | Low | Low | Low | High |
| Bucket of models | Low | Low | High | Medium | Medium | Low |
| Stacking | High | High | Low | Low | low | Medium |
