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PLS Prediction

Schrödinger extension for KNIME Workbench version by Schrödinger

Predict the dependent variable Y for a set of structures using a previously built PLS regression 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. If the column "#PLS Factors" is included in the first input table, the predicted values for each factor will only be included if that factor number has been specified. If this column is not found, all sets of predicted values will be given.

Backend implementation

canvasPLS is used to implement this node.


Column containing PLS model
Select the column containing the PLS 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 PLS Model
Data set to predict

Output Ports

Predicted Y values


Log view of PLS Prediction
Log view of PLS Prediction
Program out view of PLS Prediction
Program out view of PLS Prediction

Best Friends (Incoming)

Best Friends (Outgoing)


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