Naive Bayesian Learner
Signal Naive Bayesian Classifier
sends data only if the learning data (signal Classified Examples
is present.
This widget provides a graphical interface to the Naive Bayesian classifier.
As all widgets for classification, this widget provides a learner and classifier on the output. Learner is a learning algorithm with settings as specified by the user. It can be fed into widgets for testing learners, for instance Test Learners
. Classifier is a Naive Bayesian Classifier (a subtype of a general classifier), built from the training examples on the input. If examples are not given, the widget outputs no classifier.
![]() |
Learner can be given a name under which it will appear in, say, Next come the probability estimators. Unconditional sets the method used for estimating prior class probabilities from the data. You can use either relative frequencies, Laplace estimate or m-estimate. Conditional (discrete) sets the method for estimating conditional probabilities. You can use any of the three options above or set the method to be the same as for ``unconditional''. Finally, Conditional (continuous) specifies the method used for estimatin conditional probability for continuous attributes. We suggest LOESS; other settings are untested and may not work. When using m-estimate, the value of m parameter is set in Parameter for m-estimate. Similarly, when using LOESS, you can set the Size of LOESS window. If the class is binary, the classification accuracy may be increased considerably by letting the learner find the optimal classification threshold (option Adjust threshold). The threshold is computed from the training data. If left unchecked, the usual threshold of 0.5 is used. When you change one or more settings, you need to push Apply; this will put the new learner on the output and, if the training examples are given, construct a new classifier and output it as well. |
There are two typical uses of this widget. First, you may want to induce the model and check what it looks like in a Nomogram.
The second schema compares the results of Naive Bayesian learner with another learner, a C4.5 tree.