Fingerprint Bayesian Predictor

Predictor node to the Fingerprint Bayesian Learner node, assigning score values to test data. The input data needs to contain fingerprint descriptors as used in the corresponding learner. It computes a score for each input record by summing up the log values that are associated with the fingerprint on-bits ( sum-of-logs ). This corresponds to equation (6) in

Prediction of Biological Targets for Compounds Using Multiple-Category Bayesian Models Trained on Chemogenomics Databases , Nidhi Meir Glick, John W. Davies, and Jeremy L. Jenkins, J. Chem. Inf. Model. , 2006, 46 (3), pp 1124–1133

This score represents the confidence of a record to belong to the same category as the target category (the attribute value that was selected in the Learner node). Additionally, the node allows the user to append a crisp class prediction. This prediction is done by comparing the computed score to a threshold, whereby the threshold can be either be fixed or a value derived from the model. Details are described below.

Input ports

1. Type: Fingerprint Bayes
   Fingerprint Bayesian Model
   The fingerprint model (output of the "Fingerprint Bayesian Learner").
2. Type: Table
   Input data with fingerprint column
   The data to predict.

Output ports

1. Type: Table
   Predicted data
   The input data with class prediction appended.