CloudForest
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CloudForest | gago | |
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4 | 1 | |
735 | 869 | |
- | - | |
0.0 | 0.0 | |
about 2 years ago | about 1 year ago | |
Go | Go | |
GNU General Public License v3.0 or later | MIT License |
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CloudForest
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Trinary Decision Trees for missing value handling
I implemented something like this in a [pre xgboost boosting framework](https://github.com/ryanbressler/CloudForest) ~10 years ago and it worked well.
It isn't even that much of a speed hit using the classical sorting CART implementation. However xgboost and ligthgbm use histogram based approximate sorting which might be harder to adapt in a performant way. And certainly the code will be a lot messier.
I've got a ~10 year old implementation that does something similar calling it "three way splitting" here: https://github.com/ryanbressler/CloudForest
And i got the idea from a lab mate, Timo Erkkila's RF-ACE project though neither of us thought it was a particularly novel idea.
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[D] Best methods for imbalanced multi-class classification with high dimensional, sparse predictors
The best method i've seen for dealing with this bias is to create "artificial contrasts" by including possibly many permutated copies of each feature and then doing a statistical test of the random forest importance values for each feature vs its shuffled contrasts. This method is described here: https://www.jmlr.org/papers/volume10/tuv09a/tuv09a.pdf and there is an implementation here: https://github.com/ryanbressler/CloudForest
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