rapidsai
[FEA] Extra-trees training in cuML
Is your feature request related to a problem? Please describe. SKlearn provides training and inference for ExtraTrees regression/classification. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesRegressor.html
from sklearn.ensemble import ExtraTreesRegressor
reg = ExtraTreesRegressor(n_estimators=100, random_state=0).fit(
X_train, y_train)
reg.score(X_test, y_test)
Describe the solution you'd like It would be great to have ExtraTreeRegressor/ExtraTreeClassifier as part of cuML to use for various ensemble learning tasks.
Just collecting more info here...
@tzemicheal any ideas if we should also support bootstrap and max_samples hyper-params as supported in sklearn? Because the original paper on ET explicitly says the following: ... and that it uses the whole learning sample (rather than a bootstrap replica) to grow the trees.
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Hi @tzemicheal, First of all pardon my ignorance I am totally new to GitHub, I used to download repos and work on them locally for myself but never contributed or communicated with others here before. can I work on this issue or the work has already been done? I guess since it is an open issue that means it still need work right?
Hi @Somaya-Alshare , we always welcome contributions! Do you have experience working with CUDA / C++?
