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.
canvasBayes is used to implement this node.
- Type: Data Numerical data of the training set variables
- Type: Data Numerical data of the test set variables (identical Variable columns as defined for the Training set)
- Type: BufferedDataTable Canvas fingerprint for Training set (optional)
- Type: BufferedDataTable Canvas fingerprint for Test set (optional)
- Type: Data Bayes model
- Type: Data Statistics for training set and number of correctly predicted values for training and test sets
- Type: Data Plot data showing observed and predicted classification values for Y for both the training and test sets.