Details
-
New Feature
-
Status: Closed
-
Major
-
Resolution: Fixed
-
None
-
None
Description
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
Attachments
Attachments
Issue Links
- links to
