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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