Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/17324#discussion_r108157473
--- Diff: docs/ml-features.md ---
@@ -1284,6 +1284,64 @@ for more details on the API.
</div>
+
+## Imputer
+
+The `Imputer` transformer completes 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.
+
+**Note** all `null` values in the input columns 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
+~~~
+
+In this example, Imputer will replace all occurrences of Double.NaN (the
default for the missing value)
+with the mean (the default imputation strategy) from the other values in
the corresponding columns.
+In this example, the surrogate values for columns `a` and `b` are 3.0 and
4.0 respectively. After
+transformation, the missing values in the output columns will be replaced
by the surrogate value for
--- End diff --
"surrogate value for the relevant column."
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