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I am using in-sample OOB predictions to estimate the KL-divergence between samples. In general, OOB predictions are an efficient alternative to CV to estimate out of sample prediction performance and can be used for tuning.
Getting OOB predictions requires storing the samples used to build each tree (i.e. indiceshere). This could be made optional. We can then add up predictions for samples only that were OOB for a particualr tree here, keeping track of the number of trees for which a particular sample was OOB.
I could work on a PR, but might need some help with details and guidance on what you think the API should be.
The text was updated successfully, but these errors were encountered:
I am using in-sample OOB predictions to estimate the KL-divergence between samples. In general, OOB predictions are an efficient alternative to CV to estimate out of sample prediction performance and can be used for tuning.
Getting OOB predictions requires storing the samples used to build each tree (i.e.
indices
here). This could be made optional. We can then add up predictions for samples only that were OOB for a particualr tree here, keeping track of the number of trees for which a particular sample was OOB.I could work on a PR, but might need some help with details and guidance on what you think the API should be.
The text was updated successfully, but these errors were encountered: