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.


Append Crisp Class Prediction
If selected, the output will contain a column containing a class label. It's the target category if a record's score is larger or equal to a threshold (see below). It's some other label (non-target) if it' below the threshold.
Use threshold from model
This threshold is derived from the training data. It's the score value on the leave-one-out predicted training data that minimizes the sum of errors on the target class and the non-target classes. This value is part of the model (you can see the value by inspecting the model output).
Use fix threshold
A numeric value that is used as threshold.
Use as label for non-target predictions
If the model is specific for one particular target class (which currently is always the case as there is no multi-target fingerprint bayesian node) the class prediction for non-target classes needs to be assigned.
Default label from model
The model may contain a label of a non-target class. This is the first non-target class in the class column of the training data (good default if you have a 2-class problem).
Fix label
A custom label. Leave the field empty to use missing values.

Input Ports

The fingerprint model (output of the "Fingerprint Bayesian Learner").
The data to predict.

Output Ports

The input data with class prediction appended.


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