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linkedin / photon-ml

A scalable machine learning library on Apache Spark

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Examples for hyperparameter tuning

qingquansong opened this issue · comments

commented

Can we add some examples for using RandomSearch and BayesianOptimization functions to tune the GLMix models? Thanks!

commented

Hi @qingquansong,
BayesianOptimization is no longer supported in Photon ML at this moment. The PR #454 aims at removing hyperparameter autotuning in this repo. Grid search is still supported as the default tuning method.

commented

Thanks for the reply! Another examples for grid search? I saw that we can set a list of regularization parameter directly. But is that all we can tune?

commented

Yes, that list is about a grid search. Once you specify a list of the regularization, a list of the models in the same length will be trained and you can find the best model in the output.

commented

Just want to mention that you can specify regularization weights for each component of GLMix, such that [1, 2] for a fixed effects model, [1, 10] for a per-member model and [1, 100] for a per-job model. In this case photon-ml will do cross products and train 8 models in total.

commented

Just want to mention that you can specify regularization weights for each component of GLMix, such that [1, 2] for a fixed effects model, [1, 10] for a per-member model and [1, 100] for a per-job model. In this case photon-ml will do cross products and train 8 models in total.

Thanks for the clarification!