[jira] [Reopened] (SPARK-21594) Missing probability output from MutilayerPerceptronClassifier

["Joseph Wang (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-21594?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Joseph Wang reopened SPARK-21594:
---------------------------------

> Missing probability output from MutilayerPerceptronClassifier
> -------------------------------------------------------------
>
>                 Key: SPARK-21594
>                 URL: https://issues.apache.org/jira/browse/SPARK-21594
>             Project: Spark
>          Issue Type: New Feature
>          Components: ML
>    Affects Versions: 2.2.0
>         Environment: SPARK, PySpark,Scala, SparkR
>            Reporter: Joseph Wang
>   Original Estimate: 168h
>  Remaining Estimate: 168h
>
> The semi-supervised learning efforts have just started in Spark machine learning library.
> This is a very important direction for limited and costly labelled data.
> With the effort, the warm up time for supervised learning can be minimized.
> One of the key feature is to be able to output probability in the existing machine learning library for selecting the unlablled data by probability including self-training. The algorithm which has a tendency to overfit is particularly useful. For example, multilayer perceptron classifier(MLP) is one of the case. 
> I found this is not possible with MLP(or neural network). This is an inconsistent offering which needs to be improved. 
> thanks
> Joseph



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