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https://issues.apache.org/jira/browse/SPARK-14681?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16391762#comment-16391762
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Joseph K. Bradley commented on SPARK-14681:
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[~WeichenXu123] Thanks for the PR! I'll comment on the design here in the JIRA.
>From your PR:
{code}
class TreeClassifierStatInfo
def getLabelCount(label: Int): Double
class TreeRegressorStatInfo
def getCount(): Double
def getSum(): Double
def getSquareSum(): Double
class Node
....
+++ def statInfo: TreeStatInfo
trait TreeStatInfo
def asTreeClassifierStatInfo: TreeClassifierStatInfo
def asTreeRegressorStatInfo: TreeRegressorStatInfo
{code}
I have a few thoughts:
* I like the overall approach of using classes instead of just returning plain
double arrays.
* This will require users to explicitly cast TreeStatInfo to the
classifier/regressor type. Would it be possible to avoid that without breaking
APIs, e.g., by having a ClassificationNode and a RegressionNode inheriting from
Node?
* Naming: What about using "Stats" or "Statistics" instead of "StatInfo?" I
just feel the "Info" part is uninformative.
> Provide label/impurity stats for spark.ml decision tree nodes
> -------------------------------------------------------------
>
> Key: SPARK-14681
> URL: https://issues.apache.org/jira/browse/SPARK-14681
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Reporter: Joseph K. Bradley
> Priority: Major
>
> Currently, spark.ml decision trees provide all node info except for the
> aggregated stats about labels and impurities. This task is to provide those
> publicly. We need to choose a good API for it, so we should discuss the
> design on this issue before implementing it.
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