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From sro...@apache.org
Subject spark git commit: [DOCUMENTATION][MLLIB] typo in mllib doc
Date Thu, 03 Dec 2015 15:36:34 GMT
Repository: spark
Updated Branches:
  refs/heads/master 5349851f3 -> 7470d9edb


[DOCUMENTATION][MLLIB] typo in mllib doc

\cc mengxr

Author: Jeff Zhang <zjffdu@apache.org>

Closes #10093 from zjffdu/mllib_typo.


Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/7470d9ed
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/7470d9ed
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/7470d9ed

Branch: refs/heads/master
Commit: 7470d9edbb0a45e714c96b5d55eff30724c0653a
Parents: 5349851
Author: Jeff Zhang <zjffdu@apache.org>
Authored: Thu Dec 3 15:36:28 2015 +0000
Committer: Sean Owen <sowen@cloudera.com>
Committed: Thu Dec 3 15:36:28 2015 +0000

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 docs/ml-features.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)
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http://git-wip-us.apache.org/repos/asf/spark/blob/7470d9ed/docs/ml-features.md
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diff --git a/docs/ml-features.md b/docs/ml-features.md
index 5f88877..05c2c96 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.
 


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