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.
Selection of columns used as feature columns. Columns with nominal and numerical data are allowed.
Selection of column used as the target column. Only columns with numerical data are allowed.
Define whether missing values in the input data should be skipped or whether the node execution should fail on missing values.
Value added to the diagonal of the kernel matrix during fitting.
The kernel specifying the covariance function of the Gaussian Process. The default kernel is
ConstantKernel(1.0, constant_value_bounds="fixed") * RBF(1.0, length_scale_bounds="fixed").
Whether or not to normalize the target values
y by removing the mean and scaling
to unit-variance. This is recommended for cases where zero-mean, unit-variance priors are used.
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