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| -rw-r--r-- | docs/ml-features.md | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/docs/ml-features.md b/docs/ml-features.md index 5f88877555..05c2c96c5e 100644 --- a/docs/ml-features.md +++ b/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 an `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. Note that if the standard deviation of a feature is zero, it will return default `0.0` value in the `Vector` for that feature. |