Train a Naive Bayes classifier on data encoded using Categorical variables.
module D : Input_interfaces.Category_encoded_datainclude Classifier_interfaces.Generative
with type feature = D.feature
and type class_ = D.class_
and type feature_probability = float arrayinclude Classifier_interfaces.Classifier
with type feature = D.feature
with type class_ = D.class_val eval : t -> feature -> class_ Probabilities.teval classifier feature assign probabilities to the possible classes based upon feature.
estimate opt classes samples estimates a classifier based upon the training samples.
classes is an optional argument to specify ahead of time the possible classes to train on (defaults to the ones found in the training data). This is useful for models where we know the population domain but may not see an example of a training datum for rare cases.
opt are the optional classifier dependent estimation/evaluation arguments.
val class_probabilities :
t ->
class_ ->
float * (feature -> feature_probability)class_probabilities t class returns the prior and per feature likelihood probability (ies) learned by t for class.
val opt : ?smoothing:float -> unit -> optopt ~smoothing () the optional configuration of the classifier.