# StandardScaler

### StandardScaler

transforms a dataset of Vector rows, normalizing each feature to have unit standard deviation and/or zero mean. It takes parameters:

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 take care when applying to sparse input.

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.

```
import org.apache.spark.ml.feature.StandardScaler
val dataFrame = spark.read.format("libsvm").load("file:///opt/spark/data/mllib/sample_libsvm_data.txt")
val scaler = new StandardScaler()
.setInputCol("features")
.setOutputCol("scaledFeatures")
.setWithStd(true)
.setWithMean(false)
// Compute summary statistics by fitting the StandardScaler.
val scalerModel = scaler.fit(dataFrame)
// Normalize each feature to have unit standard deviation.
val scaledData = scalerModel.transform(dataFrame)
scaledData.show(3)

/*
Output:
+-----+--------------------+--------------------+
|label|            features|      scaledFeatures|
+-----+--------------------+--------------------+
|  0.0|(692,[127,128,129...|(692,[127,128,129...|
|  1.0|(692,[158,159,160...|(692,[158,159,160...|
|  1.0|(692,[124,125,126...|(692,[124,125,126...|
+-----+--------------------+--------------------+
*/
```


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