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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