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.

Options

Append crisp class prediction

If selected, the output will contain a column containing a class label. It is the target category if a record's score is larger or equal to a threshold (see the "Threshold" section). It is some other label (non-target) if it is below the threshold.

Threshold source

Choose whether the classification threshold is taken from the model or set to a fixed value.

Fixed: A numeric value that is used as the classification threshold.
Model: The threshold is derived from the training data: it is the score value on the leave-one-out predicted training data that minimises the sum of errors on the target class and the non-target classes. This value is part of the model (visible by inspecting the model output). Note: not available for multi-class/label models.

Fixed threshold value

A numeric value used as the classification threshold when "Threshold source" is set to "Fixed".

Label source for non-target predictions

If the model is specific for one particular target class (which is always the case as there is no multi-target fingerprint Bayesian node), the class prediction for non-target rows needs to be assigned either from the model or as a fixed custom label.

Model: The model may contain a label for the non-target class. This is the first non-target class in the class column of the training data (a good default for two-class problems).
Fixed: A custom label for non-target predictions. Leave the field empty to assign missing values to non-target rows.

Fixed label

The custom label assigned to non-target predictions. Leave this field empty to use missing values for non-target rows.

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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Installation

To use this node in KNIME, install the extension KNIME Base Chemistry Types & Nodes from the below update site following our NodePit Product and Node Installation Guide:

v5.12

A zipped version of the software site can be downloaded here.

Plugin provider: KNIME AG, Zurich, Switzerland

Plugin version: 5.12.0.v202605181345

On NodePit since: 2026-08-08

Last update: 2026-08-10

Tags: Modern UI

KNIME versions: Since v3.6

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