Add common capabilities to IClassify and IRegression interfaces

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