[jira] [Commented] (SPARK-14975) Predicted Probability per training instance for Gradient Boosted Trees in mllib.
[ https://issues.apache.org/jira/browse/SPARK-14975?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15265219#comment-15265219 ] Partha Talukder commented on SPARK-14975: - Thanks Joseph. I would keep that in mind. > Predicted Probability per training instance for Gradient Boosted Trees in > mllib. > - > > Key: SPARK-14975 > URL: https://issues.apache.org/jira/browse/SPARK-14975 > Project: Spark > Issue Type: New Feature > Components: ML >Reporter: Partha Talukder >Priority: Minor > Labels: mllib > > This function available for Logistic Regression, SVM etc. > (model.setThreshold()) but not for GBT. In comparison to "gbm" package in R, > where we can specify the distribution and get predicted probabilities or > classes. I understand that this algorithm works with "Classification" and > "Regression" algo's. Is there any way where in GBT we can get predicted > probabilities or provide thresholds to the model? -- 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-14975) Predicted Probability per training instance for Gradient Boosted Trees in mllib.
[ https://issues.apache.org/jira/browse/SPARK-14975?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Partha Talukder updated SPARK-14975: Labels: GradientBoostingTrees mllib (was: newbie) > Predicted Probability per training instance for Gradient Boosted Trees in > mllib. > - > > Key: SPARK-14975 > URL: https://issues.apache.org/jira/browse/SPARK-14975 > Project: Spark > Issue Type: Improvement >Affects Versions: 1.6.1 >Reporter: Partha Talukder > Labels: GradientBoostingTrees, mllib > > This function available for Logistic Regression, SVM etc. > (model.setThreshold()) but not for GBT. In comparison to "gbm" package in R, > where we can specify the distribution and get predicted probabilities or > classes. I understand that this algorithm works with "Classification" and > "Regression" algo's. Is there any way where in GBT we can get predicted > probabilities or provide thresholds to the model? -- 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] [Created] (SPARK-14975) Predicted Probability per training instance for Gradient Boosted Trees in mllib.
Partha Talukder created SPARK-14975: --- Summary: Predicted Probability per training instance for Gradient Boosted Trees in mllib. Key: SPARK-14975 URL: https://issues.apache.org/jira/browse/SPARK-14975 Project: Spark Issue Type: Improvement Affects Versions: 1.6.1 Reporter: Partha Talukder This function available for Logistic Regression, SVM etc. (model.setThreshold()) but not for GBT. In comparison to "gbm" package in R, where we can specify the distribution and get predicted probabilities or classes. I understand that this algorithm works with "Classification" and "Regression" algo's. Is there any way where in GBT we can get predicted probabilities or provide thresholds to the model? -- 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
