Details
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Improvement
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Status: Resolved
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Minor
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Resolution: Fixed
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None
Description
It is becoming clear that certain capabilities are common to all Regression or Classification algorithms and should be implemented once within ML_Core rather than within each algorithm. The current thoughts on the core capabilities are listed below as a basis for discussion.
Classification:
- Accuracy Statistics:
- Raw Accuracy
- Power of Discrimination (PoD) = (%correct - 1/#classes) / (1-1/#classes)
- Extended Power of Discrimination (PoDE) (%correct - %ofMostCommonClass) / (1 - %ofMostCommonClass)
- By Class:
- Recall (TP/(TP+FN)
- Precision (TP /(TP + FP))
- False Positive Rate (FP / (FP + TN))
- Note: FP = False Positive, TP = Total Positive (# correct), TN = Total Negative (#incorrect)
- Confusion Matrix
- NFold Cross Validation
Regression:
- Accuracy Stats
- RSquared
- Mean Squared Error
- Root Mean Squared Error
- ANOVA Stats
- NFold Cross Validation