Github user GayathriMurali commented on a diff in the pull request:
https://github.com/apache/spark/pull/13176#discussion_r64101972
--- Diff: docs/ml-features.md ---
@@ -1093,13 +1111,10 @@ for more details on the API.
`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.
+The bin ranges are chosen using the `approxQuantile` method based on the
Greenwald-Khanna algorithm.
+The number of bins found is equal to `numBuckets` parameter value.
`relativeError` sets the target relative precision
--- End diff --
Sure. I was not able to find API doc for `approxQuantile`
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