[jira] [Updated] (SPARK-3727) Trees and ensembles: More prediction functionality
[ https://issues.apache.org/jira/browse/SPARK-3727?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Joseph K. Bradley updated SPARK-3727: - Shepherd: (was: Joseph K. Bradley) > Trees and ensembles: 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 >Priority: Major > > 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. -- This message was sent by Atlassian JIRA (v7.6.3#76005) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-3727) Trees and ensembles: More prediction functionality
[ https://issues.apache.org/jira/browse/SPARK-3727?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Joseph K. Bradley updated SPARK-3727: - Shepherd: Joseph K. Bradley Trees and ensembles: 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. -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-3727) Trees and ensembles: More prediction functionality
[ https://issues.apache.org/jira/browse/SPARK-3727?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Joseph K. Bradley updated SPARK-3727: - Summary: Trees and ensembles: More prediction functionality (was: DecisionTree, RandomForest: More prediction functionality) Trees and ensembles: 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. -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
