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https://issues.apache.org/jira/browse/SPARK-3727?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14492906#comment-14492906
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Max Kaznady commented on SPARK-3727:
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Yes, probabilities have to be added to other models too, like
LogisticRegression. Right now they are hardcoded in two places but not
outputted in PySpark.
I think is makes sense to split into PySpark, then classification, then
probabilities, and then group different types of algorithms, all of which
output probabilities: Logistic Regression, Random Forest, etc.
Can also add probabilities for trees by counting the number of leaf 1's and 0's.
What do you think?
> DecisionTree, RandomForest: More prediction functionality
> ---------------------------------------------------------
>
> Key: SPARK-3727
> URL: https://issues.apache.org/jira/browse/SPARK-3727
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Reporter: Joseph K. Bradley
>
> DecisionTree and RandomForest currently predict the most likely label for
> classification and the mean for regression. Other info about predictions
> would be useful.
> For classification: estimated probability of each possible label
> For regression: variance of estimate
> RandomForest could also create aggregate predictions in multiple ways:
> * Predict mean or median value for regression.
> * Compute variance of estimates (across all trees) for both classification
> and regression.
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