package oml

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Parameters
Signature

package oml

oml
- Oml
  - Classification
    
    Classifier_interfaces
    
    Classifier
    
    Generative
    
    Input_interfaces
    
    Category_encoded_data
    
    Continuous_encoded_data
    
    Data
    
    Dummy_encoded_data
    
    Naive_bayes
    
    Binomial
    
    D
    
    Categorical
    
    D
    
    Performance
    
    Probabilities
  - Online
    
    Make
    
    Update
    
    Update_rules
  - Regression
    
    Interfaces
    
    Linear_model
    
    Interpolate
    
    Spline
    
    Tri_Diagonal
    
    Univariate
  - Statistics
    
    Continued_fraction
    
    Descriptive
    
    Functions
    
    Measures
    
    Sampling
    
    Poly
  - Uncategorized
    
    Estimations
    
    Matrices
    
    Solvers
    
    Vectors
  - Util
    
    Array
    
    Floatarray
    
    Float
    
    Kahan
    
    List
    
    Optional_arg_intf

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Library
Module
Module type
Parameter
Class
Class type

Train a Naive Bayes classifier on data encoded using Dummy variables.

Parameters

module D : Input_interfaces.Dummy_encoded_data

Signature

include Classifier_interfaces.Generative
  with type feature = D.feature
   and type class_ = D.class_
   and type feature_probability = float array

include Classifier_interfaces.Classifier
  with type feature = D.feature
  with type class_ = D.class_

include Input_interfaces.Data
  with type feature = D.feature
  with type class_ = D.class_

type class_ = D.class_

type feature = D.feature

include Oml_util.Optional_arg_intf

type opt

val default : opt

type t

The classifier.

val eval : t -> feature -> class_ Probabilities.t

eval classifier feature assign probabilities to the possible classes based upon feature.

type samples = (class_ * feature) list

Representing training data.

val estimate : ?opt:opt -> ?classes:class_ list -> samples -> t

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.

raises Invalid_argument
if classes are specified and new ones are found in the training samples.

raises Invalid_argument
if samples is empty.

type feature_probability = float array

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.

raises Not_found
if t never trained on class.

val opt : ?smoothing:float -> ?bernoulli:bool -> unit -> opt

opt ~smoothing ~bernoulli () the optional configuration of the classifier.

parameter bernouli
if true we treat the underlying distribution as Bernoulli (as opposed to Multinomial) and estimate the likelihood with (1-p_i) for features i that are missing from a feature when evaluated.

parameter smoothing
Additive smoothing can be applied to the final estimate of Naive Bayes classifiers. When estimating a probability distribution by counting observed instances in the feature space we may want to smooth the values, particularly if our training data is sparse.