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From "Theodore Vasiloudis (JIRA)" <j...@apache.org>
Subject [jira] [Created] (FLINK-2258) Add hyperparameter optimization to FlinkML
Date Mon, 22 Jun 2015 13:09:00 GMT
Theodore Vasiloudis created FLINK-2258:

             Summary: Add hyperparameter optimization to FlinkML
                 Key: FLINK-2258
                 URL: https://issues.apache.org/jira/browse/FLINK-2258
             Project: Flink
          Issue Type: New Feature
          Components: Machine Learning Library
            Reporter: Theodore Vasiloudis

Hyperparameter optimization is a suite of techniques that are used to find the best hyperparameters
for a machine learning model, in respect to the performance on an independent (test) dataset.

The most common way it is implemented is by using cross-validation to estimate the model performance
on the test set, and using grid search as the strategy to try out different parameters.

In the future we would like to support random search and Bayesian optimisation.

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