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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 obse

by mmq 8y ago

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 https://github.com/polyaxon/polyaxon#hyperparameters-tuning

mmq 8y ago
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 https://nni.readthedocs.io/en/latest/sklearn_examples.html
pplonski86 8y ago
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.
- minimaxir 8y ago
  > 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 8y ago
    Could you provide some link to the project?
- human_scientist 8y ago
  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/ https://automl.github.io/auto-sklearn/stable/

by mmq 8y ago

mmq 8y ago

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 https://nni.readthedocs.io/en/latest/sklearn_examples.html

pplonski86 8y ago

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.

minimaxir 8y ago
> 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 8y ago
  Could you provide some link to the project?
human_scientist 8y ago
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/ https://automl.github.io/auto-sklearn/stable/