Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/11553#discussion_r55966447
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/feature/QuantileDiscretizer.scala ---
@@ -49,6 +49,20 @@ private[feature] trait QuantileDiscretizerBase extends
Params
/** @group getParam */
def getNumBuckets: Int = getOrDefault(numBuckets)
+
+ /**
+ * Relative error (see approxQuantile documentation for description).
Must be >= 0.
+ * default: 0.01
+ * @group param
+ */
+ val relativeError = new DoubleParam(this, "relativeError", "The relative
target precision " +
+ "for approxQuantile",
+ ParamValidators.gtEq(0.0))
+ setDefault(relativeError -> 0.01)
+
+ /** @group Param */
+ def getRelativeError: Double = get(relativeError).getOrElse
+ { math.min(0.01, 1.0 / (10.0 * getNumBuckets)) }
--- End diff --
From a quick look at the paper, it does seem like a default range of `0.001
- 0.01` for eps is reasonable.
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