|Function:||Describe Naive Bayesian network of variables (classifaction)|
|Input Type:||csv, discrete|
|Input from Agent:||Dizzy, Data Selection Agent|
|Output Type:||Classification advice|
|Output to Agent:||Advice, Ceres|
Nabby is a Naive Bayes classifier. Like Rikku and Moku, it classifies a new case using the values of input variables, and it also returns the probability of the classification. Given a patient database, for example, Nabby can learn to calculate the probability for each diagnosis given a patient's symptoms.
This type of classifier is relatively simple and belongs to the group of linear classifiers. This makes it possible to show the user how each input variable value contributed to the final classification.
Instead of learning from data it is also possible to initialize Nabby with expert knowledge. Nabby can handle missing values.