zhengruifeng opened a new pull request #26547: [SPARK-29914][ML] ML models append metadata in `transform`/`transformSchema` URL: https://github.com/apache/spark/pull/26547 ### What changes were proposed in this pull request? 1, `predictionCol` in `ml.classification` & `ml.clustering` add `NominalAttribute` or `BinaryAttribute` (if `numClasses`/`k` == 2) 2, `rawPredictionCol` in `ml.classification` add `AttributeGroup` containing vectorsize=`numClasses` 3, `probabilityCol` in `ml.classification` & `ml.clustering` add `AttributeGroup` containing vectorsize=`numClasses`/`k` 4, `leafCol` in GBT/RF add `AttributeGroup` containing vectorsize=`numTrees` 5, `leafCol` in DecisionTree add `NominalAttribute` or `BinaryAttribute` (if `numLeaves` == 2) 6, `outputCol` in models in `ml.feature` add `AttributeGroup` containing vectorsize 7, `outputCol` in `UnaryTransformer`s in `ml.feature` add `AttributeGroup` containing vectorsize ### Why are the changes needed? Appened metadata can be used in downstream ops, like `Classifier.getNumClasses` There are many impls (like `Binarizer`/`Bucketizer`/`VectorAssembler`/`OneHotEncoder`/`FeatureHasher`/`HashingTF`/`VectorSlicer`/...) in `.ml` that append appropriate metadata in `transform`/`transformSchema` method. However there are also many impls return no metadata in transformation, even some metadata like `vector.size`/`numAttrs`/`attrs` can be ealily inferred. ### Does this PR introduce any user-facing change? Yes, add some metadatas in transformed dataset. ### How was this patch tested? existing testsuites and added testsuites
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