Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/7211#discussion_r36770673
--- 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
--- End diff --
Could you please put this in the Scala code tab like this:
```
<div data-lang="scala">
(you can put it here)
{% highlight scala %}
```
And then could you also please add a reference to the other APIs under
those code tabs? Thanks! (I'm trying to follow this pattern nowadays.)
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