Bayes Classification Model Building

Build a Bayes model from binary or continuous training data that can then be applied to other data sets. Both training set and testing set are required as input, these can be created using the Partitioning or Row Splitter KNIME nodes. The independent variable (X) can be either numerical or fingerprint data while the dependent variable (Y) can be categorical or numerical.

Backend implementation

canvasBayes is used to implement this node.


Column containing structure titles
Select the column containing the title for each structure
Include input columns in output data
If selected, output plot data will also contain all input columns
Output Byes model name
The name of the output Bayes model should be entered here
X variables
Select the independent X variables for the analysis
Canvas Fingerprint
Select one Fingerprint column (if available) as an independent X variables for the analysis
Y variable
Select the dependent Y variable (must not be the same as any X variable)
Y type options
Select an option for the type of data defined by the Y variable.
* Y is numeric—The Y variable has numeric values. Click Bins to assign the number and type of bins.
* Y is categorical—Y is treated as a list of categories. This option should be selected for Y variables that are strings.
Advanced Options button
Click this button to set Kullback-Leibler acceptance criteria for fingerprint data, and the smoothing coefficient.

Input Ports

Numerical data of the training set variables
Numerical data of the test set variables (identical Variable columns as defined for the Training set)
Canvas fingerprint for Training set (optional)
Canvas fingerprint for Test set (optional)

Output Ports

Bayes model
Statistics for training set and number of correctly predicted values for training and test sets
Plot data showing observed and predicted classification values for Y for both the training and test sets.




  • No workflows found



You want to see the source code for this node? Click the following button and we’ll use our super-powers to find it for you.