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https://issues.apache.org/jira/browse/FLINK-2108?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14610382#comment-14610382
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Theodore Vasiloudis commented on FLINK-2108:
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Turns out it's more complicated to have a score function that is available for
chained Predictors as well.
If score is defined as a function of a Predictor subclass, such as Classifier,
then it will not be available to a chained Classifier, since the chaining will
produce a Predictor.
If we define score in the Predictor instead, we will need to provide an
implementation for ChainedPredictor as well, since that extends Predictor.
The only way forward then if we want to have a score function, is to follow the
Operation paradigm, and have implicit score operations that get attached to
concrete predictors, and define a default one for ChainedPredictor as well.
> Add score function for Predictors
> ---------------------------------
>
> Key: FLINK-2108
> URL: https://issues.apache.org/jira/browse/FLINK-2108
> Project: Flink
> Issue Type: Improvement
> Components: Machine Learning Library
> Reporter: Theodore Vasiloudis
> Assignee: Theodore Vasiloudis
> Priority: Minor
> Labels: ML
>
> A score function for Predictor implementations should take a DataSet[(I, O)]
> and an (optional) scoring measure and return a score.
> The DataSet[(I, O)] would probably be the output of the predict function.
> For example in MultipleLinearRegression, we can call predict on a labeled
> dataset, get back predictions for each item in the data, and then call score
> with the resulting dataset as an argument and we should get back a score for
> the prediction quality, such as the R^2 score.
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