# ClassifierFunction

represents a function generated by Classify that classifies data into classes.

# Details and Options

• ClassifierFunction works like Function.
• ClassifierFunction[][data] attempts to classify data, returning the class in which data is considered most likely to be.
• ClassifierFunction[][{data1,data2,}] attempts to classify all the datai.
• ClassifierFunction[][data,prop] gives the specified property of the classification associated with data.
• Possible properties applicable to all methods include:
•  "Decision" best class according to the probabilities and the utility function "TopProbabilities" probabilities for most likely classes "TopProbabilities"n probabilities for the n most likely classes "Probability"class probability for a specific class "Probabilities" association of probabilities for all possible classes "Properties" list of all properties available
• ClassifierFunction[][data,,opts] specifies that the classifier should use the options opts when applied to data.
• Possible options are:
•  ClassPriors Automatic explicit prior probabilities for classes IndeterminateThreshold Automatic below what probability to return Indeterminate PerformanceGoal Automatic which aspect of performance to optimize UtilityFunction Automatic utility expressed as a function of actual and predicted class
• A trained in an older version of the Wolfram Language will still work in the current version.
• Classify[net] can be used to convert a NetChain or NetGraph representing a classifier into a .

# Examples

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## Basic Examples(2)

Create a ClassifierFunction with Classify and a list of labeled examples:

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Classify an unlabeled example with the ClassifierFunction:

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Classify multiple examples:

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Return the probabilities of the classes given the feature of an example:

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Return the sorted probabilities of the most likely classes:

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Return the probability of the most probable class:

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Return the probability of a given class:

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Plot the probability of class "B" as a function of the feature:

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Generate a ClassifierFunction using multiple features:

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Use the function on a new example:

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Classify an example that has missing features:

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Get the probabilities for the most probable classes:

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