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Using random search for efficient hyper-parameters optimization with H2O


PyData Amsterdam 2016

Optimizing hyper-parameters is a common yet time-consuming task for machine learning practitioners. Previous studies have shown that, when compared to traditional strategies like manual search and grid search, random search can achieve equal performance in a computationally efficient manner. In this talk I will demonstrate the random search feature in H2O with machine learning examples based on publicly available datasets.

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