ActiveClassification

ActiveClassification[f,{conf1,conf2,}]

gives an object representing the result of active classification obtained by using the function f to determine classes for the example configurations confi.

ActiveClassification[f,reg]

generates configurations within the region specified by reg.

ActiveClassification[f,sampler]

generates configurations by applying the function sampler.

ActiveClassification[f,{conf1,conf2,}nsampler]

applies the function nsampler to successively generate configurations starting from one of the confi.

Details and Options

  • ActiveClassification[] returns an ActiveClassificationObject[] whose properties can be obtained using ActiveClassificationObject[]["prop"].
  • Possible properties include:
  • "EvaluationHistory"configurations explored and classes assigned to them
    "Method"method used for active classification
    "ClassifierFunction"best ClassifierFunction[] obtained
    "ClassifierMeasurementsObject"latest ClassifierMeasurementsObject[] obtained
    "LearningCurve"plot of mean cross-entropy evolution
    "Properties"list of all available properties
  • Configurations can be of any form accepted by Classify (single data element, list of data elements, association of data elements, etc.) and of any type accepted by Classify (numerical, textual, sounds, images, etc.).
  • When applied to a configuration conf, the output of the function f is interpreted as a label.
  • In ActiveClassification[f,spec], spec defines the domain of the function f. A domain can be defined by a list of configurations, a geometric region or a configuration generator function.
  • In ActiveClassification[f,sampler], sampler[] must output a configuration suitable for f to be applied to it.
  • In ActiveClassification[f,{conf1,conf2,}nsampler], nsampler[conf] must output a configuration.
  • ActiveClassification has the same options as Classify, with the following additions and changes:
  • InitialEvaluationHistoryNoneinitial set of configurations and classes
    MaxIterations2000maximum number of iterations
    MethodAutomaticmethod used to determine configurations to query and the classification algorithm to use
  • Possible settings for Method include:
  • Automaticautomatically choose method
    "Randomized"choose random configurations from the domain
    "MaxEntropy"choose configurations for which the classifier has maximum uncertainty
    assocassociation specifying the evaluation strategy and classification method
  • In the form Methodassoc, the association can have elements:
  • "EvaluationStrategy"method for determining which configurations to query
    "ClassificationMethod"method to use for classification

Examples

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Basic Examples  (3)

Train an ActiveClassificationObject[] to classify whether an integer is greater than 50:

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Extract the resulting classifier:

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Classify new examples:

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Train a classification object with the domain defined by an interval:

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Extract the classifier:

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Classify new examples:

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Train a classification object to classify whether a matrix is positive semidefinite, with the domain defined by a configuration generator:

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Extract the classifier:

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Classify new examples:

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Scope  (2)

Options  (3)

Applications  (3)

Possible Issues  (1)

See Also

Classify  ActiveClassificationObject  BayesianMinimization  BayesianMaximization

Introduced in 2017
(11.1)