Opened 8 years ago
Closed 7 years ago
#2622 closed enhancement (duplicate)
Handle missing values (i.e. NaN) correctly when training GBT models
| Reported by: | gkronber | Owned by: | gkronber |
|---|---|---|---|
| Priority: | medium | Milestone: | HeuristicLab 3.3.x Backlog |
| Component: | Algorithms.DataAnalysis | Version: | 3.3.13 |
| Keywords: | Cc: |
Description
Right now GBT training only uses comparison operators and assumes that there are no missing values in the input variables. This is not really correct as NaN values are assigned to either the left or the right subtree. Instead those observations should be ignored in the training phase.
This ticket is related to #2612.
Change History (3)
comment:1 Changed 8 years ago by gkronber
- Milestone changed from HeuristicLab 3.3.15 to HeuristicLab 3.3.x Backlog
comment:2 Changed 7 years ago by gkronber
comment:3 Changed 7 years ago by gkronber
- Resolution set to duplicate
- Status changed from new to closed
Yes, this is a duplicate.
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Duplicate of #2613?