performance measures difference between predictions and data
performance(results, outcome, measure)Numeric vector with predictions
Numeric vector same length as results with real data to compare to.
Optional length one character vector that is either:
"accuracy", "sens", "spec", or "ppv". This specifies what measure of
predictive performance to use for training and evaluating the model. The
default measure is "accuracy". However, accuracy can be a problematic
measure when the classes are imbalanced in the samples, i.e. if a class the
model is trying to predict is very rare. Alternatives to accuracy are
available that illuminate different aspects of predictive power. Sensitivity
answers the question, “ given that a result is truly an event, what is the
probability that the model will predict an event?” Specificity answers the
question, “given that a result is truly not an event, what is the
probability that the model will predict a negative?” Positive predictive
value answers, “what is the percent of predicted positives that are
actually positive?”
Returns a numeric vector length one.
This is the function of the datafsm package used to measure the fsm model performance. It uses the caret package.