MATLAB File Help: prtClassNaiveBayes
 prtClassNaiveBayes Naive Bayes Classifier
     CLASSIFIER = prtClassNaiveBayes returns a Naive Bayes Classifier.
     CLASSIFIER = prtClassFld(PROPERTY1, VALUE1, ...) constructs a
     prtClassNaiveBayes object CLASSIFIER with properties as specified by
     PROPERTY/VALUE pairs.
     A prtClassNaiveBayes object inherits all properties from the abstract class
     prtClass. In addition is has the following properties:
     baseRv             - The base type of random variable to be used in
                          training the model; baseRv is of type prtRv.
                          By default baseRv is a prtRvMvn.
     A naive Bayes classification algorithm learns a distribution for the
     data under each hypothesis and assumes independence between the data
     features (columns) to simplify inference.  
     A prtClassNaiveBayes object inherits the TRAIN, RUN, CROSSVALIDATE and
     KFOLDS methods from prtAction. It also inherits the PLOT method from
     TestDataSet = prtDataGenUniModal;       % Create some test and
     TrainingDataSet = prtDataGenUniModal;   % training data
     classifier = prtClassNaiveBayes;           % Create a classifier
     classifier = classifier.train(TrainingDataSet);    % Train
     classified = run(classifier, TestDataSet);         % Test
     [pf,pd] = prtScoreRoc(classified,TestDataSet);
     h = plot(pf,pd,'linewidth',3);
     title('ROC'); xlabel('Pf'); ylabel('Pd');
See also
Class Details
Superclasses prtClass
Sealed false
Construct on load false
Constructor Summary
prtClassNaiveBayes Naive Bayes Classifier 
Property Summary
baseRv The base randon variable 
dataSet The training prtDataSet, only stored if verboseStorage is true.  
dataSetSummary Structure that summarizes prtDataSet. 
internalDecider Optional prtDecider object for making decisions 
isCrossValidateValid True 
isNativeMary true 
isSupervised True 
isTrained Indicates if prtAction object has been trained. 
naiveRv The naive random variable 
name Naive Bayes Classifier 
nameAbbreviation NBC 
twoClassParadigm Whether the classifier retures one output (binary) or two outputs (m-ary) when there are only two unique class labels 
userData User specified data 
verboseStorage Specifies whether or not to store the training prtDataset. 
Method Summary
  crossValidate Cross validate prtAction using prtDataSet and cross validation keys. 
  get get the object properties 
  kfolds Perform K-folds cross-validation of prtAction 
  optimize Optimize action parameter by exhaustive function maximization. 
  plot Plot the output confidence of a prtClass object 
  run Run a prtAction object on a prtDataSet object. 
  set set the object properties 
  train Train a prtAction object using training a prtDataSet object.