({{, ClassifierInformation[classifier]
generates a report giving information on the classifier function classifier.}, {, ClassifierInformation[classifier,prop] 
gives information for classifier associated with property prop.}})

OpenersNotesSection(Spacer1)Details

The classifier is typically a (RefLink[ClassifierFunction, paclet:ref/ClassifierFunction]) object as generated by (RefLink[Classify, paclet:ref/Classify]).

Properties available from ClassifierInformation typically include:

OpenersPrimaryExamplesSection(Spacer1)Examples  (6)

OpenersExampleSection0(Spacer1)Basic Examples  (2)

Train a classifier:

c=Classify[{1->"A",2->"A",3->"B",4->"A",5->"B",6->"B"}]

Generate an information report about the classifier:

ClassifierInformation[c]

Obtain information properties available for this classifier:

ClassifierInformation[c, "Properties"]

Classification properties available for this classifier:

ClassifierInformation[c, "FunctionProperties"]

List of classes that the classifier can return:

ClassifierInformation[c, "Classes"]

Time that (RefLink[Classify, paclet:ref/Classify]) spent on training:

ClassifierInformation[c, "TrainingTime"]

Check how the features have been interpreted:

ClassifierInformation[c, "FeatureTypes"]

Train a classifier:

c=Classify[ExampleData[{"MachineLearning","UCILetter"},"Data"]]

Display the information panel:

ClassifierInformation[c]

Only display the learning curve:

ClassifierInformation[c,"LearningCurve"]

OpenersExampleSection1(Spacer1)Scope  (2)

Train a logistic classifier:

c=Classify[{1->"a",2->"a",3->"b",4->"b"},Method->"LogisticRegression"]

Return a pure function for the classification task:

ClassifierInformation[c,"Function"]

Return a pure function giving class probabilities:

pf=ClassifierInformation[c,"ProbabilitiesFunction"]

Train a nearest neighbors classifier:

c=Classify[{1->True,2->True,3-> False,4->False}, Method-> "NearestNeighbors"]

Get a short description of the method:

ClassifierInformation[c, "MethodDescription"]

Obtain few parameters about the classifier:

Dataset[AssociationMap[ClassifierInformation[c, #]& , {"NeighborsNumber", "NearestMethod","DistributionSmoothing"}]]

OpenersExampleSection3(Spacer1)Applications  (2)

Train an SVM classifier:

trainingset={1->"A",2->"A",3->"B",4->"A",5->"B",6->"B"};

c=Classify[trainingset,Method->"SupportVectorMachine"]

Obtain the time used to train the classifier:

ClassifierInformation[c,"TrainingTime"]

Obtain a set of method suboptions that can be used to retrain the classifier:

methodoption=ClassifierInformation[c,"MethodOption"]

Use these options to retrain the classifier:

c=Classify[trainingset, methodoption]

This training has been faster:

ClassifierInformation[c, "TrainingTime"]

Train a classifier:

c=Classify[{1,2,3,4}->{"yes","yes","no","no"}]

Extract the utility function of the classifier:

utility=ClassifierInformation[c,"UtilityFunction"]

Modify the utility to penalize examples of class "yes" being misclassified as "no":

utility["yes","no"] = -100;
utility

Predict an example with the original and new utility function:

{c[2.52],c[2.52,"UtilityFunction"->utility]}

({{DocumentationFooterIconsRelatedFunction.png, ({{See Also}, {(RefLink[ClassifierMeasurements, paclet:ref/ClassifierMeasurements])  ▪  (RefLink[Classify, paclet:ref/Classify])}})}})