[
https://issues.apache.org/jira/browse/SPARK-2222?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14040101#comment-14040101
]
Alexander Ulanov commented on SPARK-2222:
-----------------------------------------
The micro averaged Precision and Recall are equal for multiclass classifier,
because sum(fn(i))=sum(fp(i)), i.e. they are just the sum of all non-diagonal
elements in confusion matrix. For more details please refer to the book
"Introduction to IR" by Manning.
> Add multiclass evaluation metrics
> ---------------------------------
>
> Key: SPARK-2222
> URL: https://issues.apache.org/jira/browse/SPARK-2222
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Affects Versions: 1.0.0
> Reporter: Alexander Ulanov
>
> There is no class in Spark MLlib for measuring the performance of multiclass
> classifiers. This task involves adding such class and unit tests. The
> following measures are to be implemented: per class, micro averaged and
> weighted averaged Precision, Recall and F1-Measure.
--
This message was sent by Atlassian JIRA
(v6.2#6252)
