Evaluation – Test Routines

classim Classify image using a given classifier more routines
classc Convert mapping to classifier
labeld Find labels of objects by classification
cleval Classifier evaluation (learning curve)
clevalb Classifier evaluation (learning curve), bootstrap version
clevalf Classifier evaluation (feature size curve)
clevals Classifier evaluation (feature /learning curve), bootstrap
confmat Computation of confusion matrix
costm Cost mapping, classification using costs
prcrossval Crossvalidation
disperror Display error matrix with information on classifiers and datasets
labelim Construct image of labeled pixels
loso Leave_one_set_out crossvalidation
mclassc Computation of multi-class classifier from 2-class discriminants
reject Compute error-reject trade-off curve
prroc Receiver-operator curve (ROC)
shiftop Shift operating point of classifier
testc General error estimation routine for trained classifiers
testd Error of dataset applied to given classifier
testauc Estimate error as area under the ROC

elements: datasets datafiles cells and doubles mappings classifiers mapping types.
operations: datasets datafiles cells and doubles mappings classifiers stacked parallel sequential dyadic.
user commands: datasets representation classifiers evaluation clustering examples support routines.
introductory examples: Introduction Scatterplots Datasets Datafiles Mappings Classifiers Evaluation Learning curves Feature curves Dimension reduction Combining classifiers Dissimilarities.
advanced examples.