[jira] [Created] (SPARK-23409) RandomForest/DecisionTree (syntactic) pruning of redundant subtrees

["Alessandro Solimando (JIRA)" <ji...@apache.org>]

Alessandro Solimando created SPARK-23409:
--------------------------------------------

             Summary: 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


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}




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