[
https://issues.apache.org/jira/browse/SPARK-23409?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Alessandro Solimando updated SPARK-23409:
-----------------------------------------
Description:
Improvement: redundancy elimination from decision trees where all the leaves of
a given subtree share the same prediction.
Benefits:
* Model interpretability
* Faster unitary model invocation (relevant for massive number of invocations)
* Smaller model memory footprint
For instance, consider the following decision tree.
{panel:title=Original Decision Tree}
{noformat}
DecisionTreeClassificationModel (uid=dtc_e794a5a3aa9e) of depth 3 with 15 nodes
If (feature 1 <= 0.5)
If (feature 2 <= 0.5)
If (feature 0 <= 0.5)
Predict: 0.0
Else (feature 0 > 0.5)
Predict: 0.0
Else (feature 2 > 0.5)
If (feature 0 <= 0.5)
Predict: 0.0
Else (feature 0 > 0.5)
Predict: 0.0
Else (feature 1 > 0.5)
If (feature 2 <= 0.5)
If (feature 0 <= 0.5)
Predict: 1.0
Else (feature 0 > 0.5)
Predict: 1.0
Else (feature 2 > 0.5)
If (feature 0 <= 0.5)
Predict: 0.0
Else (feature 0 > 0.5)
Predict: 0.0
{noformat}
{panel}
The proposed method, taken as input the first tree, aims at producing as output
the following (semantically equivalent) tree:
{panel:title=Pruned Decision Tree}
{noformat}
DecisionTreeClassificationModel (uid=dtc_e794a5a3aa9e) of depth 3 with 15 nodes
If (feature 1 <= 0.5)
Predict: 0.0
Else (feature 1 > 0.5)
If (feature 2 <= 0.5)
Predict: 1.0
Else (feature 2 > 0.5)
Predict: 0.0
{noformat}
{panel}
was:
Improvement: redundancy elimination from decision trees where all the leaves of
a given subtree share the same prediction.
Benefits:
* Model interpretability
* Faster unitary model invocation (relevant for massive )
* Smaller model memory footprint
For instance, consider the following decision tree.
{panel:title=Original Decision Tree}
{noformat}
DecisionTreeClassificationModel (uid=dtc_e794a5a3aa9e) of depth 3 with 15 nodes
If (feature 1 <= 0.5)
If (feature 2 <= 0.5)
If (feature 0 <= 0.5)
Predict: 0.0
Else (feature 0 > 0.5)
Predict: 0.0
Else (feature 2 > 0.5)
If (feature 0 <= 0.5)
Predict: 0.0
Else (feature 0 > 0.5)
Predict: 0.0
Else (feature 1 > 0.5)
If (feature 2 <= 0.5)
If (feature 0 <= 0.5)
Predict: 1.0
Else (feature 0 > 0.5)
Predict: 1.0
Else (feature 2 > 0.5)
If (feature 0 <= 0.5)
Predict: 0.0
Else (feature 0 > 0.5)
Predict: 0.0
{noformat}
{panel}
The proposed method, taken as input the first tree, aims at producing as output
the following (semantically equivalent) tree:
{panel:title=Pruned Decision Tree}
{noformat}
DecisionTreeClassificationModel (uid=dtc_e794a5a3aa9e) of depth 3 with 15 nodes
If (feature 1 <= 0.5)
Predict: 0.0
Else (feature 1 > 0.5)
If (feature 2 <= 0.5)
Predict: 1.0
Else (feature 2 > 0.5)
Predict: 0.0
{noformat}
{panel}
> RandomForest/DecisionTree (syntactic) pruning of redundant subtrees
> -------------------------------------------------------------------
>
> Key: SPARK-23409
> URL: https://issues.apache.org/jira/browse/SPARK-23409
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Affects Versions: 2.2.1
> Environment:
> Reporter: Alessandro Solimando
> Priority: Minor
>
> Improvement: redundancy elimination from decision trees where all the leaves
> of a given subtree share the same prediction.
> Benefits:
> * Model interpretability
> * Faster unitary model invocation (relevant for massive number of
> invocations)
> * Smaller model memory footprint
> For instance, consider the following decision tree.
> {panel:title=Original Decision Tree}
> {noformat}
> DecisionTreeClassificationModel (uid=dtc_e794a5a3aa9e) of depth 3 with 15
> nodes
> If (feature 1 <= 0.5)
> If (feature 2 <= 0.5)
> If (feature 0 <= 0.5)
> Predict: 0.0
> Else (feature 0 > 0.5)
> Predict: 0.0
> Else (feature 2 > 0.5)
> If (feature 0 <= 0.5)
> Predict: 0.0
> Else (feature 0 > 0.5)
> Predict: 0.0
> Else (feature 1 > 0.5)
> If (feature 2 <= 0.5)
> If (feature 0 <= 0.5)
> Predict: 1.0
> Else (feature 0 > 0.5)
> Predict: 1.0
> Else (feature 2 > 0.5)
> If (feature 0 <= 0.5)
> Predict: 0.0
> Else (feature 0 > 0.5)
> Predict: 0.0
> {noformat}
> {panel}
> The proposed method, taken as input the first tree, aims at producing as
> output the following (semantically equivalent) tree:
> {panel:title=Pruned Decision Tree}
> {noformat}
> DecisionTreeClassificationModel (uid=dtc_e794a5a3aa9e) of depth 3 with 15
> nodes
> If (feature 1 <= 0.5)
> Predict: 0.0
> Else (feature 1 > 0.5)
> If (feature 2 <= 0.5)
> Predict: 1.0
> Else (feature 2 > 0.5)
> Predict: 0.0
> {noformat}
> {panel}
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