Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/17324#discussion_r107299250
--- Diff: docs/ml-features.md ---
@@ -1284,6 +1284,61 @@ for more details on the API.
</div>
+
+## Imputer
+
+Imputation transformer for completing missing values in the dataset,
either using the mean or the
+median of the columns in which the missing value are located. The input
columns should be of
+DoubleType or FloatType. Currently Imputer does not support categorical
features and possibly
+creates incorrect values for a categorical feature. All Null values in the
input column are
+treated as missing, and so are also imputed.
+
+**Examples**
+
+Suppose that we have a DataFrame with the column `a` and `b`:
+
+~~~
+ a | b
+------------|-----------
+ 1.0 | Double.NaN
+ 2.0 | Double.NaN
+ Double.NaN | 3.0
+ 4.0 | 4.0
+ 5.0 | 5.0
+~~~
+
+By default, Imputer will replace all the `Double.NaN` (missing value) with
the mean (strategy) from
+other values in the corresponding columns. In our example, the surrogates
for `a` and `b` are 3.0
+and 4.0 respectively. After transformation, the output columns will not
contain missing value anymore.
--- End diff --
Perhaps "After transformation, the missing values in the output columns
will be replaced by the surrogate value computed for that column"?
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