[streetcat1]

scikit learn is a different type of search, hence it will not be supported by this tool or any DNN search tool.

DNN require an architecture search, I.e. the building block are full layers, depth of the network, optimizer etc.

scikit learn search a parameter space, I.e. the algorithm weight are much much simpler and few.

So to sum up, DNN search involve big building blocks while scikit learn search (or for that reason any "classical ML" algorithm) is more of a parameter search.

[ The actual sci kit learn search would also include pre processing steps, which can be seen as a separate block]

Also, note that that DNN search is much more expensive than scikit learn search (100X) ]

[human_scientist]

Automatically building a scikit learn estimator might include many conditional hyperparameters and also a very large amount of them (<100) [1]. However, performing joint architecture and hyperparameter search can be framed to be on a much simpler search space, e.g., for a recent paper that aims to automate the design of RNA molecules, we formulated a 14 dimensional search space which includes very little conditional hyperparameters [2].

The tools included in the repository are very broadly applicable and only a few of them are specifically targeted at neural architecture search.

[1] https://www.kdnuggets.com/2016/08/winning-automl-challenge-a... [2] https://openreview.net/forum?id=ByfyHh05tQ

[williamsmj]

This tool absolutely supports scikit-learn. Please see the docs. https://nni.readthedocs.io/en/latest/sklearn_examples.html.