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From thvasilo <...@git.apache.org>
Subject [GitHub] flink pull request: [FLINK-2157] [ml] Create evaluation framework ...
Date Thu, 21 Apr 2016 15:38:35 GMT
Github user thvasilo commented on the pull request:

    I did some testing and I think the problem has to do with the types that each scaler expects.
    `StandardScaler` has fit and transform operations for `DataSets` of type `Vector`, `LabeledVector`,
and `(T :< Vector, Double)` while `MinMaxScaler` does not provide one for `(T :< Vector,
Double)`. If you add the operations the code runs fine (at least re. you first comment).
    So this is a bug unrelated to this PR I think. The question becomes if we want to support
all three of these types. My recommendation would be to have support for `Vector` and `LabeledVector`
only, and remove all operations that work on `(Vector, Double)` tuples. I will file a JIRA
for that.
    There is an argument to be whether some pre-processing steps are supervised (e.g. [PCA
vs. LDA](https://stats.stackexchange.com/questions/161362/supervised-dimensionality-reduction))
but in the strict definition of a transformer we shouldn't care about the label, only the
features, so that operation can implemented at the `Transformer` level.

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