Constructs a NaiveBayesClassifier.

Returns the classification probability error for the input pattern and known target classification.

Create a nominal attribute and the number of categories

Determines whether the specified Object is equal to the current Object.

Allows an Object to attempt to free resources and perform other cleanup operations before the Object is reclaimed by garbage collection.

Returns the number of patterns for each target classification.

Returns the classification probability errors for each pattern in the training data.

Serves as a hash function for a particular type.

Returns a table of means for each continuous attribute in continuousData segmented by the target classes in classificationData.

Returns the predicted classification for each training pattern.

Returns the predicted classification probabilities for each target class.

Returns a table of standard deviations for each continuous attribute in continuousData segmented by the target classes in classificationData.

Returns a table of classification errors of non-missing classifications for each target classification plus the overall total of classification errors.

Gets the Type of the current instance.

Specifies whether or not missing values will be ignored during the training process.

Creates a shallow copy of the current Object.

Predicts the classification for the input pattern using the trained Naive Bayes classifier.

Predicts the classification probabilities for the input pattern using the trained Naive Bayes classifier.

Returns a String that represents the current Object.

Trains a Naive Bayes classifier for classifying data into one of nClasses target classifications.

Parameter for calculating smoothed estimates of conditional probabilities for continuous attributes.

Parameter for calculating smoothed estimates of conditional probabilities for discrete (nominal) attributes.

Specifies the replacement value to be used for conditional probabilities equal to zero.