Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/13176#discussion_r64100666
--- 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.
--- End diff --
See []() - I think we can say something like
```
The bin ranges are chosen using an approximate algorithm (see the
documentation for approxQuantile for a detailed description).
```
We could link the the `approxQuantile` to the relevant API doc link.
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