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Posted to issues@spark.apache.org by "Badari Madhav (JIRA)" <ji...@apache.org> on 2015/07/27 18:44:05 UTC
[jira] [Comment Edited] (SPARK-9312) The multiclass logistic
regression and the OneVsRest model do not provide predictive probability
(confidence factor) along with the prediction
[ https://issues.apache.org/jira/browse/SPARK-9312?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14642984#comment-14642984 ]
Badari Madhav edited comment on SPARK-9312 at 7/27/15 4:43 PM:
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1. WRT OneVsRest I agree that the rawPrediction column should be interpreted as "confidence" rather than probabilities but it is still very useful to determine a threshold for the classifier. I will create a separate JIRA & PR combination for OVR.
2. For the Logistic regression, I am using the LogisticRegressionWithLBFGS for model generation. I don't think the spark.ml.LogisticRegression is compatible with LogisticRegressionWithLBFGS. If it is, I can use the spark.ml version.
was (Author: badriub):
1. WRT OneVsRest I agree that the rawPrediction column should be interpreted as "confidence" rather than probabilities but it is still very useful to determine a threshold for the classifier. I will create a separate JIRA & PR combination for OVR.
2. For the Logistic regression, I am planning to use it in a pipeline and I guess it would be preferable to use the class under spark.ml. But I would think it would be useful to have the api for pred prob in spark.mllib. Would that be preferable or should I not bother making changes in spark.mllib.LogisticRegressionModel?
> The multiclass logistic regression and the OneVsRest model do not provide predictive probability (confidence factor) along with the prediction
> ----------------------------------------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-9312
> URL: https://issues.apache.org/jira/browse/SPARK-9312
> Project: Spark
> Issue Type: Improvement
> Components: ML, MLlib
> Affects Versions: 1.4.0, 1.4.1
> Reporter: Badari Madhav
> Labels: features
> Original Estimate: 72h
> Remaining Estimate: 72h
>
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