Sklearn Gaussian Process Regression Learner (Labs)

Learns Gaussian Process Regression implemented by scikit-learn library.

The implementation follows the algorithm in section 2.1 of the paper Gaussian Processes for Machine Learning by Carl E. Rasmussen and Christopher K.I. Williams (2006).

The model is trained with the selected numerical target column, and feature columns (can be numerical or nominal) from the input table. By default, the rightmost numerical column is selected as the target column and all the remaining numerical columns are selected as features.

Input ports

1. Type: Table
   Input table

   Numerical and nominal columns can be selected as feature columns from this table, and the target column must be numerical.

Output ports

1. Type: org.knime.python3.nodes.PythonBinaryBlobFileStorePortObject
   Trained Model

   Trained Gaussian process regression model.