NaiveBayesMultinomialUpdateable (3.7)


Class for building and using a multinomial Naive Bayes classifier

For more information see,

Andrew Mccallum, Kamal Nigam: A Comparison of Event Models for Naive Bayes Text Classification.In: AAAI-98 Workshop on 'Learning for Text Categorization', 1998.

The core equation for this classifier:

P[Ci|D] = (P[D|Ci] x P[Ci]) / P[D] (Bayes rule)

where Ci is class i and D is a document.

Incremental version of the algorithm.

(based on WEKA 3.7)

For further options, click the 'More' - button in the dialog.

All weka dialogs have a panel where you can specify classifier-specific parameters.

Input Ports

  1. Type: Data Training data

Output Ports

  1. Type: Weka 3.7 Classifier Trained model

Find here

Analytics > Mining > Weka > Weka (3.7) > Classification Algorithms > bayes

Make sure to have this extension installed:

KNIME Weka Data Mining Integration (3.7)

Update site for KNIME Analytics Platform 3.7:
KNIME Analytics Platform 3.7 Update Site

How to install extensions