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

$SCHRODINGER/utilities/canvasBayes
canvasBayes is used to implement this node.

Input ports

1. Type: Table
   Training set variables
   Numerical data of the training set variables
2. Type: Table
   Test set variables
   Numerical data of the test set variables (identical Variable columns as defined for the Training set)
3. Type: Table
   Canvas FingerPrint for training set
   Canvas fingerprint for Training set (optional)
4. Type: Table
   Canvas FingerPrint for test set
   Canvas fingerprint for Test set (optional)

Output ports

1. Type: Table
   Bayes model
   Bayes model
2. Type: Table
   Statistics
   Statistics for training set and number of correctly predicted values for training and test sets
3. Type: Table
   Plot data
   Plot data showing observed and predicted classification values for Y for both the training and test sets.