Learns Gaussian Process Classification based on Laplace approximation implemented by scikit-learn library.
The implementation follows the algorithm in sections 3.1, 3.2 and 5.1 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 nominal target column, and feature columns (can be nominal or numerical) from the input table. By default, the rightmost nominal column is selected as the target column and all the remaining nominal and 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 nominal data are allowed.
Define whether missing values in the input data should be skipped or whether the node execution should fail on missing values.
Available options:
The kernel specifying the covariance function of the GP. The default kernel
1.0 * RBF(1.0)
is used as default.
Available options:
Multi-class classification method selection.
Available options:
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