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From "Theodore Vasiloudis (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-2108) Add score function for Predictors
Date Wed, 01 Jul 2015 14:37:04 GMT

    [ https://issues.apache.org/jira/browse/FLINK-2108?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14610382#comment-14610382

Theodore Vasiloudis commented on FLINK-2108:

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

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