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New Split Criteria Seem to Outperform Existing Criteria in Some Simulations #2

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morgsmss7 opened this issue Feb 3, 2020 · 1 comment

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morgsmss7 commented Feb 3, 2020

My plan for the semester will consist of 3 main tasks:

PR existing split criteria comparison into scikit-learn
Clean up axis and oblique projection split criteria from last semester.

Write more tests (random state, memory efficiency, shared weights)
Implement shared weights (existing code for this exists, but I abandoned it last semester because I was getting errors which turned out to be unrelated. I would simply like to revert to this version, fix what ended up being the error after all, and make sure it works)

Write a conference paper about these new splitters and how they compare against existing splitters in different simulations.

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morgsmss7 commented Feb 10, 2020

Part 1 will be my official Sprint 1 goal, but I'm hoping I will be able to finish 2 as a reach goal for this semester.

morgsmss7 mentioned this issue

Nonlinear regression simulations for existing split criteria NeuroDataDesign/scikit-learn#29

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