I am trying to compare model accuracy between several different measurement metrics. For example, some citations use accuracy while other use error. That one is rather obvious, but there are lots of different metrics and I am not entirely sure how to compare some of them and not lose some of the individual metrics integrity. Or whether or not some can be compared at all. The list I have is:
Error Rate - Mean Absolute Error - Absolute Error - Log-Loss - Classification Accuracy - Root Mean Squared Error - Classification Error - F-Measure - Area Under Curve - Mean Test Error - Error Percentage - Misclassification Error - Test Error - Mean Test Error
So my question is how to effectively convert between these, and if no direct conversion is possible, to compare and rank in a meaningful and accurate way.