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Now there is random forest classification in framework. I want to propose add also random forest regression
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Currently all random forest arguments are listed individually; we could use (...) to pass on RF arguments automatically. I dont think we need to specify them in the function call
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![picture](https://github.com/ElijahAgunbiade/Portfolio/assets/173221971/cbcc899d-c2b1-47fc-bed2-d16f1ee81161)
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I have programmed for **Random Forest Classification** in both R and Python.
Please help me create a explanatory markdown documentation for the same.
Link for the code is [here](https://github.c…
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This is the secuence of my commands:
ML.FOREST.ADD myforestx 0 . CATEGORIC sex "male" .L LEAF 1 .R LEAF 0
ML.FOREST.RUN myforestx sex:male
ML.FOREST.RUN myforestx sex:female
But my runs always …
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Resources
- https://github.com/h2oai/h2o-3/blob/master/h2o-docs/src/product/tutorials/rf/rf.md
- https://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/drf.html
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This is as much a proposal as a question:
Does it make sense to use out-of-bag samples for estimating the distributions in the leaves? That should give you a better estimate of uncertainty, right?
I t…
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https://colab.research.google.com/drive/1hNO8g2w5T1U2gLsunLgJZqsnrwq13uLK?usp=sharing
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