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https://issues.apache.org/jira/browse/MADLIB-907?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15269328#comment-15269328
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ASF GitHub Bot commented on MADLIB-907:
---------------------------------------

GitHub user orhankislal opened a pull request:

    https://github.com/apache/incubator-madlib/pull/41

    Prediction Metrics: New module

    JIRA: MADLIB-907
    A collection of summary statistics to gauge model accuracy
    based on predicted values vs. ground-truth values.

You can merge this pull request into a Git repository by running:

    $ git pull https://github.com/orhankislal/incubator-madlib 
feature/pred_metrics

Alternatively you can review and apply these changes as the patch at:

    https://github.com/apache/incubator-madlib/pull/41.patch

To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:

    This closes #41
    
----

----


> Prediction Metrics
> ------------------
>
>                 Key: MADLIB-907
>                 URL: https://issues.apache.org/jira/browse/MADLIB-907
>             Project: Apache MADlib
>          Issue Type: New Feature
>          Components: Module: Utilities
>            Reporter: Frank McQuillan
>            Assignee: Orhan Kislal
>             Fix For: v1.9.1
>
>         Attachments: interface_v1.sql, interface_v3.sql
>
>
> Story
> As a data scientist, I want to compute prediction metrics on my data, so that 
> I can gauge model accuracy based on predicted values vs. actual values.
> 1)  The PDL Tools modules "Prediction Metrics" [1] is an example of what 
> could be ported to MADlib.  Source code is located at [2].
> 2) Here is functionality from PDL tools to use as a starting point:
>       mf_mae
>       Mean Absolute Error. 
>  
>       mf_mape
>       Mean Absolute Percentage Error. 
>  
>       mf_mpe
>       Mean Percentage Error. 
>  
>       mf_rmse
>       Root Mean Square Error. 
>  
>       mf_r2
>       R-squared. 
>  
>       mf_adjusted_r2
>       Adjusted R-squared. 
>  
>       mf_binary_classifier
>       Metrics for binary classification. 
>  
>       mf_auc
>       Area under the ROC curve (in binary classification). 
>  
>       mf_confusion_matrix
>       Confusion matrix for a multi-class classifier. 
> References
> [1]  PDL Tools Prediction Metrics module
> http://pivotalsoftware.github.io/PDLTools/group__grp__prediction__metrics.html
> [2] PDL tools source code
> https://github.com/pivotalsoftware/PDLTools



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