[performance suggestions?]Parallelism btw trees and replace linear fit to SGD with batch? about linear-tree HOT 2 CLOSED

Hi,

1. LinearTree and LinearForest can be used with n_jobs using joblibs. Like any sklearn model, n_jobs=-1 means use all cores while n_jobs=2 means use two cores. If your machine has only two cores, it will run only on them

2. sklearn.linear_model.SGDClassifier/Regressor can be used as base_estimator in LinearTree. Performances depend on your task.

3. GPU training is not supported (like in sklearn).

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Thanks, but I made it work with 3(CUDA) by rewriting linear class with coef_ and intercept_ and score attributes using pytorch

But I guess: 1. for small amount of data, this is indeed very slow.(Use TorchMatrixBasedLinearRegression(device='cuda') for small ones
2. maybe I should publish this "TorchLinearRegression" to another package?

update:
linear-tree does run with these if we write the classes in a sklearn format
https://www.kaggle.com/code/hli111111/sklearn-in-pytorch-with-gpu

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