misclassificationPenalties {llama}

Misclassification penalty

Description

Calculates the penalty incurred because of making incorrect decisions, i.e. choosing suboptimal algorithms.

Usage

misclassificationPenalties(data, model, addCosts = NULL)

Arguments

data

the data used to induce the model. The same as given to classify, classifyPairs, cluster or regression.

model

the algorithm selection model. Can be either a model returned by one of the model-building functions or a function that returns predictions such as vbs or the predictor function of a trained model.

addCosts

does nothing. Only here for compatibility with the other evaluation functions.

Details

Compares the performance of the respective chosen algorithm to the performance of the best algorithm for each datum. Returns the absolute difference. This denotes the penalty for choosing a suboptimal algorithm, e.g. the additional time required to solve a problem or reduction in solution quality incurred. The misclassification penalty of the virtual best is always zero.

If the model returns NA (e.g. because no algorithm solved the instance), 0 is returned as misclassification penalty.

data may contain a train/test partition or not. This makes a difference when computing the misclassification penalties for the single best algorithm. If no train/test split is present, the single best algorithm is determined on the entire data. If it is present, the single best algorithm is determined on each test partition. That is, the single best is local to the partition and may vary across partitions.

Value

A list of the misclassification penalties.

Author(s)

Lars Kotthoff

See Also

parscores, successes

Examples

if(Sys.getenv("RUN_EXPENSIVE") == "true") {
data(satsolvers)
folds = cvFolds(satsolvers)

model = classify(classifier=makeLearner("classif.J48"), data=folds)
sum(misclassificationPenalties(folds, model))
}