[mmq]

I think it should probably support scikit as well as any other library, since it's only making suggestions of hyper-parameters based on recorded/historical observations or random evaluations.

At least that's the behaviour of the platform[1] I am working on.

[1]: https://github.com/polyaxon/polyaxon#hyperparameters-tuning

[pplonski86]

I think it all depends on the purpose of the library and who is a target user. The NNI is a package for tuning neural networks models, it will be mostly used in use cases that require deep neural networks, like image classification or voice recognition.

BTW, I think all autoML solutions forget about end users. They all require too much engineering knowledge from the user. I think it will be nice to have an autoML solution that can be used by citizen data scientist.

[human_scientist]

What about approaches like auto-sklearn [1]? With these it is basicaly:

  >>> automl = autosklearn.classification.AutoSklearnClassifier()
  >>> automl.fit(X_train, y_train)
  >>> y_hat = automl.predict(X_test)

[1] https://automl.github.io/auto-sklearn/stable/

[minimaxir]

> BTW, I think all autoML solutions forget about end users. They all require too much engineering knowledge from the user. I think it will be nice to have an autoML solution that can be used by citizen data scientist.

This is the approach of a project I am currently working on. (and am now explicitly making clear in the README!)

[pplonski86]

Could you provide some link to the project?

[mmq]

UPDATE: Looking at the docs, there's an example[1] using this library with scikit-learn.

[1]: https://nni.readthedocs.io/en/latest/sklearn_examples.html