Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/15149#discussion_r79583415
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala ---
@@ -176,10 +176,26 @@ private[ml] trait HasThreshold extends Params {
private[ml] trait HasThresholds extends Params {
/**
- * Param for Thresholds in multi-class classification to adjust the
probability of predicting each class. Array must have length equal to the
number of classes, with values >= 0. The class with largest value p/t is
predicted, where p is the original probability of that class and t is the
class' threshold.
+ * Defines "thresholds" for each class. These do not, actually, define
the minimum
+ * probability per class for that class to be chosen. They act like like
'cutoff' values in
+ * R's
[[https://cran.r-project.org/web/packages/randomForest/randomForest.pdf
randomForest]]
+ * package, which ironically are also not cutoffs. They define a sort of
inverse weight, and if
+ * defined, the class that is chosen is the one whose ratio of class
probability to threshold
--- End diff --
This doc is good but we essentially say "the class with highest p/t is
chosen" twice - once here and again in the paragraph below. Perhaps we can
consolidate?
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