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

Options

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.

Views

LogFile

LogFile

StdOut/Err

StdOut/Err

Workflows

• No workflows found

Links

• No links available

Developers