Alessandro Solimando created SPARK-23409:
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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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