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Posted to issues@spark.apache.org by "Yan Facai (颜发才 JIRA)" <ji...@apache.org> on 2017/04/23 05:04:04 UTC
[jira] [Updated] (SPARK-16957) Use weighted midpoints for split
values.
[ https://issues.apache.org/jira/browse/SPARK-16957?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Yan Facai (颜发才) updated SPARK-16957:
------------------------------------
Description:
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}
was:
Just like R's gbm, 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}
> 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
> Priority: Trivial
>
> 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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