Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/7211#discussion_r36770671
--- 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
--- End diff --
Could you please make this render as Latex? You can follow the examples in
mllib-linear-methods.md
Same for other equations below.
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