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https://issues.apache.org/jira/browse/MADLIB-907?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15271174#comment-15271174
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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/42
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_take2
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/incubator-madlib/pull/42.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 #42
----
commit 0a2900a315d3d19068ef8acbe782bbc00bca24cb
Author: Orhan Kislal <[email protected]>
Date: 2016-05-04T18:36:21Z
Prediction Metrics: New module
JIRA: MADLIB-907
A collection of summary statistics to gauge model accuracy
based on predicted values vs. ground-truth values.
----
> 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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