Github user GayathriMurali commented on a diff in the pull request:
https://github.com/apache/spark/pull/13176#discussion_r64476690
--- Diff: docs/ml-features.md ---
@@ -1098,9 +1098,9 @@ for more details on the API.
`QuantileDiscretizer` takes a column with continuous features and outputs
a column with binned
categorical features. The number of bins is set by the `numBuckets`
parameter.
-The bin ranges are chosen using an approximate algorithm (see the
documentation for
[approxQuantile](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala)
+The bin ranges are chosen using an approximate algorithm (see the
documentation for
[approxQuantile](api/scala/index.html#org.apache.spark.sql.DataFrameStatFunctions.scala)
for a detailed description). The precision of the approximation can be
controlled with the
-`relativeError` parameter. When set to zero, exact quantiles are
calculated.
+`relativeError` parameter. When set to zero, exact quantiles are
calculated. Computing exact quantiles is an expensive operation.
The lower and upper bin bounds will be `-Infinity` and `+Infinity`
covering all real values.
**Examples**
--- End diff --
@MLnick The example is still valid for the default value of relativeError
param(0.001). I will it as is
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