Update normalization parameters and add estimator params validation

[avolkov-intel]

Description

• Add different normalization options
• Remove implicit normalization from loaders
• Add estimator parameters validation

Estimator parameters validation can be useful in case you want to override some parameter like n_jobs using -p algorithm:estimator_params:n_jobs=64 for the benchmarks. Currently it would fail since some estimators don't support n_jobs and you would need to run the benchmarks separately. In this approach this parameter will be simply ignored by estimators that don't support it and warning will be shown.

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Checklist:

Completeness and readability

• I have commented my code, particularly in hard-to-understand areas.
• I have updated the documentation to reflect the changes or created a separate PR with updates and provided its number in the description, if necessary.
• Git commit message contains an appropriate signed-off-by string (see CONTRIBUTING.md for details).
• I have resolved any merge conflicts that might occur with the base branch.

Testing

• I have run it locally and tested the changes extensively.
• All CI jobs are green or I have provided justification why they aren't.
• I have extended testing suite if new functionality was introduced in this PR.

[ethanglaser]

Seems like this is the only case where benchmark behavior changes - is it intended?

[avolkov-intel]

I think it was done for a reason but let me check the convergence for both options

[avolkov-intel]

Essentially, there's no effect on the result: accuracy and number of iterations stays the same

[ethanglaser]

Suggested change

	"dataset" : "cifar",
	"split_kwargs": { "ignore" : true },
	"dataset": "cifar",
	"split_kwargs": { "ignore": true },

[ethanglaser]

LGTM once David's comments are resolved. We should add a json linter step to CI and unify formatting in sklbench configs.

[david-cortes-intel]

LGTM, but could also change array.values to array.to_numpy().