Github user GayathriMurali commented on a diff in the pull request:
https://github.com/apache/spark/pull/13176#discussion_r65790125
--- Diff: docs/ml-features.md ---
@@ -1092,14 +1095,11 @@ for more details on the API.
## QuantileDiscretizer
`QuantileDiscretizer` takes a column with continuous features and outputs
a column with binned
-categorical features.
-The bin ranges are chosen by taking a sample of the data and dividing it
into roughly equal parts.
-The lower and upper bin bounds will be `-Infinity` and `+Infinity`,
covering all real values.
-This attempts to find `numBuckets` partitions based on a sample of the
given input data, but it may
-find fewer depending on the data sample values.
-
-Note that the result may be different every time you run it, since the
sample strategy behind it is
-non-deterministic.
+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](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 (**Note:** Computing exact quantiles is an expensive operation). The
default value of `relativeError` is 0.01.
--- End diff --
@MLnick I specified the default value coz in the example, we say "however
in most cases the default parameter value should suffice " and not mentioning
the default value wouldnt make much sense.
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