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.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
