Details & Suboptions

The "ClassDistribution" method learns a probability distribution for each class by applying LearnDistribution on the examples of this class. When given a new example to classify, the class probabilities of the example are computed by measuring the probability density function (PDF ) of the example for each class distribution. More precisely, the probabilities are computed using Bayes's theorem , where x is the example to classify, is the prior probability of the class, and is the PDF of x for the class distribution.
The following option can be given:
Method Automatic method to be used by LearnDistribution
In Method method, method can be any method of LearnDistribution , possibly with options and suboption specifications.
Classify […,AnomalyDetector Inherited] can be used to use the implicit mixture distribution learned by this method in order to detect an anomalous example.

Examples

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Train a classifier function on labeled examples:

Classify a new example:

Obtain probabilities:

Obtain information about the classifier:

Obtain specific information about the method used by LearnDistribution :

Generate some normally distributed data:

Visualize it:

Train a classifier on this dataset:

Plot the training set and the probability distribution of each class as a function of the features:

Train a classifier and specify that the anomaly detector should be inherited from the "ClassDistributions" method:

Classify a new example:

Classify a new example that is anomalous:

Train a classifier function and specify that the "KernelDensityEstimation" method of LearnDistribution should be used:

Obtain the class probabilities for a new example:

Train another classifier and specify some options of the "KernelDensityEstimation" method:

Obtain the class probabilities for a new example: