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prtEvalMinCost

prtEvalMinCost Returns the minimum cost of classification cost = prtEvalMinCost(prtClassifier, prtDataSet, costMat) returns the minimum cost of classification of dataset prtDataSet with classifier prtClassifier according to the cost matrix costMat. prtDataSet must be a labeled, prtDataSetStandard object. prtClassifier must be a prtClass object. costMat must be a 2x2 matrix consisting of the costs. costMat = [C00, C10; C01 C11], where Cij is the cost for deciding i when j is the truth. cost = prtEvalMinCost(prtClassifier, prtDataSet,costMat, nFolds) returns the minimum cost of classification of dataset prtDataSet with classifier prtClassifier according to the cost matrix costMat with K-fold cross-validation. prtDataSet must be a labeled, binary prtDataSetStandard object. prtClassifier must be a prtClass object. nFolds is the number of folds in the K-fold cross-validation. cost = prtEvalMinCost(prtClassifier, prtDataSet,costMat, xValInds) same as above, but use crossValidation with specified indices instead of random folds. [cost, PF, PD] = prtScoreMinCost(...) returns the probability of false alarm PF and the probability of detection PD. Example: dataSet = prtDataGenSpiral; classifier = prtClassDlrt; cost = prtEvalMinCost(classifier, dataSet,[ 0 1; 1 0])