Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/7211#discussion_r36770690
--- Diff: docs/ml-features.md ---
@@ -905,6 +906,74 @@ scaledData = scalerModel.transform(dataFrame)
</div>
</div>
+## MinMaxScaler
+
+`MinMaxScaler` transforms a dataset of `Vector` rows, rescaling each
feature to a specific range (often [0, 1]). It takes parameters:
+
+* `min`: 0.0 by default. Lower bound after transformation, shared by all
features.
+* `max`: 1.0 by default. Upper bound after transformation, shared by all
features.
+
+`MinMaxScaler` computes summary statistics on a data set and produces a
`MinMaxScalerModel`. The model can then transform each feature individually
such that it is in the given range.
+
+The rescaled value for a feature E is calculated as,
+
+ Rescaled(e_i) = \frac{e_i - E_{min}}{E_{max} - E_{min}} * (max - min) +
min
+
+For the case E_{max} == E_{min}, Rescaled(e_i) = 0.5 * (max + min)
+
+Note that since zero values will probably be transformed to non-zero
values, output of the transformer will be DenseVector even for sparse input.
+
+More details can be found in the API docs for
+[MinMaxScaler](api/scala/index.html#org.apache.spark.ml.feature.MinMaxScaler)
and
+[MinMaxScalerModel](api/scala/index.html#org.apache.spark.ml.feature.MinMaxScalerModel).
+
+The following example demonstrates how to load a dataset in libsvm format
and then rescale each feature to [0, 1].
+
+<div class="codetabs">
+<div data-lang="scala">
+{% highlight scala %}
+import org.apache.spark.ml.feature.MinMaxScaler
+import org.apache.spark.mllib.util.MLUtils
+
+val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
+val dataFrame = sqlContext.createDataFrame(data)
+val scaler = new MinMaxScaler()
+ .setInputCol("features")
+ .setOutputCol("scaledFeatures")
+
+// 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)
+{% endhighlight %}
+</div>
+
+<div data-lang="java">
+{% highlight java %}
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.ml.feature.MinMaxScaler;
+import org.apache.spark.ml.feature.MinMaxScalerModel;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.util.MLUtils;
+import org.apache.spark.sql.DataFrame;
+
+JavaRDD<LabeledPoint> data =
+ MLUtils.loadLibSVMFile(jsc.sc(),
"data/mllib/sample_libsvm_data.txt").toJavaRDD();
+DataFrame dataFrame = jsql.createDataFrame(data, LabeledPoint.class);
+MinMaxScaler scaler = new MinMaxScaler()
+ .setInputCol("features")
+ .setOutputCol("scaledFeatures");
+
+// Compute summary statistics by fitting the StandardScaler
+MinMaxScalerModel scalerModel = scaler.fit(dataFrame);
+
+// Normalize each feature to have unit standard deviation.
--- End diff --
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