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Bayes Classification Prediction

Schrödinger extension for KNIME Workbench version 19.3.13.201908262231 by Schrödinger

Predict the dependent variable Y for a set of structures using a previously built Bayes model and the required independent X variables. Ensure the independent variables used to build the model are included in the second input table for each structure or as a Fingerprint column in the third input table.

Backend implementation

utilities/canvasBayes
canvasBayes is used to implement this node.

Options

Column containing Bayes model
Select the column containing the Bayes model
Column containing structure title
Select the column containing the structure names
Include input columns in output data
If selected, output data will also contain all input columns
Y variable
Select the dependent Y variable in the second input table to compare against predicted values. Leave unselected if these values are unknown.

Input Ports

Canvas Bayes Model
Data set to predict
Canvas Fingerprint for dataset to predict (optional)

Output Ports

Predicted Y values

Views

Log view of Bayes Prediction
Log view of Bayes Prediction

Installation

To use this node in KNIME, install Schrödinger Extensions for KNIME from the following update site:

KNIME 4.0
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Developers

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