Github user BenFradet commented on a diff in the pull request:
https://github.com/apache/spark/pull/10093#discussion_r46472147
--- Diff: docs/ml-features.md ---
@@ -1232,7 +1232,7 @@ lInfNormData = normalizer.transform(dataFrame,
{normalizer.p: float("inf")})
* `withStd`: True by default. Scales the data to unit standard deviation.
* `withMean`: False by default. Centers the data with mean before scaling.
It will build a dense output, so this does not work on sparse input and will
raise an exception.
-`StandardScaler` is a `Model` which can be `fit` on a dataset to produce a
`StandardScalerModel`; this amounts to computing summary statistics. The model
can then transform a `Vector` column in a dataset to have unit standard
deviation and/or zero mean features.
+`StandardScaler` is a `Estimator` which can be `fit` on a dataset to
produce a `StandardScalerModel`; this amounts to computing summary statistics.
The model can then transform a `Vector` column in a dataset to have unit
standard deviation and/or zero mean features.
--- End diff --
an `Estimator`
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