I’m wondering if anyone has been able to implement a conditional logistic solution in XGBoost (even for 1:m matching) - either through transforming data to fit one of the existing objective functions, or creating a custom objective function?
I understand that there is the rank:pairwise objective function but I wanted to avoid this function for two reasons:
It doesn’t allow for cross-validation as it cannot handle having the ‘group’ information being populated
The output of the rank:pairwise is open to interpretation - I need to the output to be the conditional probability within a group, whereas am I led to understand the output of this model is just some type of raw score used solely for ranking within a group.
Conditional logistic has similarities to both multi:softmax and survival:cox. From what I understand about multi:softmax though it deals with multiple outcome possibilities for each record, whereas I need one successful case across several records in each group. I keep reading the conditional logistic is kind of like a special case of survival:cox but I am unsure of how to pass the ‘group’ index through to the survival:cox function.
Is it possible somehow to pass the group ID through to a custom objective function? I see that the get.info for ‘group’ doesn’t work for a DMatrix so I am not sure of the most efficient way to do this.
I’d be extremely grateful if anyone has pointers on how best to approach this.