Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/7150#discussion_r37000954
--- Diff: python/pyspark/ml/feature.py ---
@@ -1030,6 +1030,87 @@ class Word2VecModel(JavaModel):
"""
+@inherit_doc
+class MinMaxScaler(JavaEstimator, HasInputCol, HasOutputCol):
+ """
+ Rescale each feature individually to a common range [min, max]
linearly using column summary
+ statistics, which is also known as min-max normalization or Rescaling.
The rescaled value for
+ 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)
--- End diff --
Please copy full Scala doc: "Note that since zero values will probably be
transformed to non-zero values, output of the transformer will be DenseVector
even for sparse input."
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