DESCRIPTION

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Class for a Naive Bayes classifier using estimator classes. It is a probabilistic reasoning model based on applying Bayes' theorem. In many practical applications, parameter estimation for naive Bayes models uses the method of maximum likelihood; in other words, one can work with the naive Bayes model without believing in Bayesian probability or using any Bayesian methods. On the other hand, is possible to use a kernel-based estimator instead, in order to calculate probabilities .

PARAMETERS

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Classifier Name: Element identifier for operations and Clipboard.

Use Kernel estimator: By default, the Naive Bayes classifier uses a maximum likelihood estimator, enabling this parameter a kernel-based estimator is used instead.

Use supervised discretization: This parameter permits to convert numeric attributes to nominal ones, base on a supervised discretization algorithm.

REFERENCES

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[1]George H. John, Pat Langley: Estimating Continuous Distributions in Bayesian Classifiers. In: Eleventh Conference on Uncertainty in Artificial Intelligence, San Mateo, 338-345, 1995