Together with the corresponding loop end node a boosting loop can be constructed. It repeatedly trains simple models and weights them according to their classification error. The algorithm used is AdaBoost.SAMME, i.e. is can also cope with multi-class problems. The first output contains the re- and over-sampled dataset, rows that have been predicted wrong are contained more often than correctly predicted rows.
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02_Scaling_Analytics_w_BigDataKNIME Hub- AI_project_Group2KNIME Hub
- BoostingKNIME Hub
- Group_5_AI_CleanKNIME Hub
- Group_5_AI_Clean_LucasKNIME Hub
- Group_5_AI_Karl_(NotFinal)KNIME Hub
- Group_5_AI_Karl_(NotFinal)KNIME Hub
- Group_5_AI_KostasKNIME Hub
- Group_5_AI_LatestVersionKNIME Hub
- Group_5_AI_LatestVersion(2)KNIME Hub
- Group_5_AI_PatrickKNIME Hub
- Group_5_WorkflowKNIME Hub
- Tree_EnsemblesKNIME Hub
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