[jira] [Closed] (MADLIB-907) Prediction Metrics

["Frank McQuillan (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/MADLIB-907?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Frank McQuillan closed MADLIB-907.
----------------------------------

> 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: MADlib_ Prediction_Metrics_user_doc_v2.pdf, 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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