NodeBayes Classification Model Building

Learner

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

Output Ports

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