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Ive found that uplift models can be quite fragile but I was surprised experimenting with the Lenta dataset how a method like class transformation performs so well (in fact the first decile produces more uplift in terms of responders than the full campaign, even at the volume in the first decile as is) and many other technique (I experimented with S and T learners and several uplift RF) fail to get close or just plain fail. Curious if this is your experience or there is something with this data that you suspect could be happening....
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@AllardJM hi there! Thank you for sharing your experience. Mostly we faced the same result with the Lenta dataset and the class transformation model. We even won a hackathon with this dataset + this model couple years ago. More to say is that initially an author of the class transformation algorithm report that target split should be half and half: 50% of “target =1”. But in reality target split can be any.
Benchmarks for uplift models are really a place to study.
Ive found that uplift models can be quite fragile but I was surprised experimenting with the Lenta dataset how a method like class transformation performs so well (in fact the first decile produces more uplift in terms of responders than the full campaign, even at the volume in the first decile as is) and many other technique (I experimented with S and T learners and several uplift RF) fail to get close or just plain fail. Curious if this is your experience or there is something with this data that you suspect could be happening....
The text was updated successfully, but these errors were encountered: