Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/6451#discussion_r31208098
--- Diff: docs/ml-features.md ---
@@ -789,6 +789,90 @@ scaledData = scalerModel.transform(dataFrame)
</div>
</div>
+## Bucketizer
+
+`Bucketizer` transforms a column of continuous features to a column of
feature buckets, where the buckets are specified by users. It takes a parameter:
+
+* `splits`: Parameter for mapping continuous features into buckets. With
n+1 splits, there are n buckets. A bucket defined by splits x,y holds values in
the range [x,y) except the last bucket, which also includes y. Splits should be
strictly increasing. Values at -inf, inf must be explicitly provided to cover
all Double values; Otherwise, values outside the splits specified will be
treated as errors. Two examples of `splits` are `Array(Double.NegativeInfinity,
0.0, 1.0, Double.PositiveInfinity)` and `Array(0.0, 1.0, 2.0)`.
+
+Note that if you have no idea of the upper bound and lower bound of the
targeted column, you would better add the `Double.NegativeInfinity` and
`Double.PositiveInfinity` as the bounds of your splits to prevent a potenial
out of Bucketizer bounds exception.
+
+Note also that the splits that you provided have to be in strictly
increasing order, i.e. `s0 < s1 < s2 < ... < sn`.
+
+More details can be found in the API docs for
[Bucketizer](api/scala/index.html#org.apache.spark.ml.feature.Bucketizer).
+
+The following example demonstrates how to bucketize a column of `Double`s
into another index-wised column.
+
+<div class="codetabs">
+<div data-lang="scala">
+{% highlight scala %}
+import org.apache.spark.ml.feature.Bucketizer
+import org.apache.spark.sql.DataFrame
+
+// Since we know the bounds of data, there is no need to add -inf and inf.
--- End diff --
Could you actually have these examples use -inf and inf? I'm afraid some
people might copy the code without thinking and be confused when they get
out-of-bounds errors.
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