agoda-com/spark-hpopt
Bayesian hyperparamter tuning for Spark MLLib
This framework helps machine learning practitioners efficiently find the best settings for their models. You provide your model and the ranges for its adjustable parameters, and it outputs the optimal parameter combination that yields the best model performance. This is for data scientists and machine learning engineers who train models using Apache Spark's MLLib.
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Use this if you are building machine learning models on Apache Spark and need an automated, intelligent way to optimize their hyperparameters.
Not ideal if you are not using Apache Spark for your machine learning workflows or prefer manual hyperparameter tuning.
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Feb 10, 2021
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