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scikit-learn_bench benchmarks various implementations of machine learning algorithms across data analytics frameworks. It currently support the scikit-learn, DAAL4PY, cuML, and XGBoost frameworks for...

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This is initial suggestion in purpose to start the discussion. Parameters for device context and Scikit-Learn patching are added. Some benchmarks are changed in purpose to use new parameters.

Enabling of heterogeneous data support for all kinds of benchmarks

@oleksandr-pavlyk This quick PR addresses the following - Allow n_jobs in sklearn and only use it if daal is not present - Makefile: use rbf kernel for SVM by default...

Using ``kernel='linear'`` in ``SVC`` basically makes no sense. You should be using ``LinearSVC`` and probably set ``dual=False``.

It would be great if you could do benchmarks with different data set sizes and with tall, wide and parse data, where possible, and report where these are not supported...

As discussed here: https://github.com/scikit-learn/scikit-learn/pull/14247#issuecomment-508166336 I think the current report is very hard to read. It might be helpful to specify very clearly what the baseline is, that is the meaning...

The cases will be moved to [skl_xpu_config.json](https://github.com/IntelPython/scikit-learn_bench/blob/master/configs/skl_xpu_config.json)

When device=cpu is used, I get the following error. This happens on every benchmark. I have not had any issues running the same on other systems (Intel Xeon Sapphire Rapids...

# Benchmarks rework **Entry points: [README](https://github.com/Alexsandruss/scikit-learn_bench/tree/dev/refactor/README.md), [Developer Guide](https://github.com/Alexsandruss/scikit-learn_bench/blob/dev/refactor/docs/README.md)** ## Key features: - Benchmarks runner, report generator and separate benchmarks are implemented to run as python modules - Benchmarking config specification...