[jira] [Updated] (SPARK-16957) Use weighted midpoints for split values.

["Sean Owen (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-16957?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sean Owen updated SPARK-16957:
------------------------------
    Priority: Minor  (was: Trivial)

> Use weighted midpoints for split values.
> ----------------------------------------
>
>                 Key: SPARK-16957
>                 URL: https://issues.apache.org/jira/browse/SPARK-16957
>             Project: Spark
>          Issue Type: Improvement
>          Components: MLlib
>            Reporter: Vladimir Feinberg
>            Assignee: Yan Facai (颜发才)
>            Priority: Minor
>             Fix For: 2.3.0
>
>
> We should be using weighted split points rather than the actual continuous binned feature values. For instance, in a dataset containing binary features (that are fed in as continuous ones), our splits are selected as {{x <= 0.0}} and {{x > 0.0}}. For any real data with some smoothness qualities, this is asymptotically bad compared to GBM's approach. The split point should be a weighted split point of the two values of the "innermost" feature bins; e.g., if there are 30 {{x = 0}} and 10 {{x = 1}}, the above split should be at {{0.75}}.
> Example:
> {code}
> +--------+--------+-----+-----+
> |feature0|feature1|label|count|
> +--------+--------+-----+-----+
> |     0.0|     0.0|  0.0|   23|
> |     1.0|     0.0|  0.0|    2|
> |     0.0|     0.0|  1.0|    2|
> |     0.0|     1.0|  0.0|    7|
> |     1.0|     0.0|  1.0|   23|
> |     0.0|     1.0|  1.0|   18|
> |     1.0|     1.0|  1.0|    7|
> |     1.0|     1.0|  0.0|   18|
> +--------+--------+-----+-----+
> DecisionTreeRegressionModel (uid=dtr_01ae90d489b1) of depth 2 with 7 nodes
>   If (feature 0 <= 0.0)
>    If (feature 1 <= 0.0)
>     Predict: -0.56
>    Else (feature 1 > 0.0)
>     Predict: 0.29333333333333333
>   Else (feature 0 > 0.0)
>    If (feature 1 <= 0.0)
>     Predict: 0.56
>    Else (feature 1 > 0.0)
>     Predict: -0.29333333333333333
> {code}



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