Adding more classifier implementations - XGBoost and support for more scoring functions (precision, recall, other custom metric etc.)

dclambert / pyensemble

An implementation of Caruana et al's Ensemble Selection algorithm in Python, based on scikit-learn

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Adding more classifier implementations - XGBoost and support for more scoring functions (precision, recall, other custom metric etc.) #3

Open shivam6294 opened 7 years ago

shivam6294 commented 7 years ago

Hey! Great work on simplifying the process of building ensembles. I was trying to use this, but I found that the GBM implementation of Scikit learn is far too slow for my needs. We could possibly include a faster implementation, aka XGBoost.

I would love to help out with this, will make a PR soon after adding XGBoost. Also keen on helping you add more scoring functions (precision, recall, matthew's coefficient, and support for custom scoring functions similar to the scikit learn api)

Cheers!

joofeloof commented 7 years ago

I'm pretty sure I have this working on the classifier side if you see my fork. It's not the cleanest but maybe it will be useful to you? I never had time to clean things up and do a PR, but it doesn't seem like dclambert is actively working on this given the other open pulls?