[jira] [Created] (FLINK-2259) Support training Estimators using a (train, validation, test) split of the available data

["Theodore Vasiloudis (JIRA)" <ji...@apache.org>]

Theodore Vasiloudis created FLINK-2259:
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             Summary: Support training Estimators using a (train, validation, test) split of the available data
                 Key: FLINK-2259
                 URL: https://issues.apache.org/jira/browse/FLINK-2259
             Project: Flink
          Issue Type: New Feature
          Components: Machine Learning Library
            Reporter: Theodore Vasiloudis
            Priority: Minor


When there is an abundance of data available, a good way to train models is to split the available data into 3 parts: Train, Validation and Test.

We use the Train data to train the model, the Validation part is used to estimate the test error and select hyperparameters, and the Test is used to evaluate the performance of the model, and assess its generalization [1]

This is a common approach when training Artificial Neural Networks, and a good strategy to choose in data-rich environments. Therefore we should have some support of this data-analysis process in our Estimators.

[1] Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. The elements of statistical learning. Vol. 1. Springer, Berlin: Springer series in statistics, 2001.



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