Spark ML/MLlib has provided featureImportances <https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala#L213> to estimate the importance of each feature.
2015-10-28 18:29 GMT+08:00 Eugen Cepoi <[email protected]>: > Hey, > > Is there some kind of "explain" feature implemented in mllib for the > algorithms based on tree ensembles? > Some method to which you would feed in a single feature vector and it > would return/print what features contributed to the decision or how much > each feature contributed "negatively" and "positively" to the decision. > > This can be very useful to debug a model on some specific samples and for > feature engineering. > > Thanks, > Eugen >
