Measure Model Performance

performance measures difference between predictions and data

performance(results, outcome, measure)

Arguments

results	Numeric vector with predictions
outcome	Numeric vector same length as results with real data to compare to.
measure	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?''

Value

Returns a numeric vector length one.

Details

This is the function of the datafsm package used to measure the fsm model performance. It uses the caret package.