Opened 12 months ago

Last modified 2 months ago

#2622 new enhancement

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 (1)

comment:1 Changed 2 months ago by gkronber

  • Milestone changed from HeuristicLab 3.3.15 to HeuristicLab 3.3.x Backlog
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