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.

Options

Input

Feature columns

Selection of columns used as feature columns. Columns with nominal and numerical data are allowed.

Target column

Selection of column used as the target column. Only columns with numerical data are allowed.

If there are missing values in the features

Define whether missing values in the input data should be skipped or whether the node execution should fail on missing values.

Available options:

  • Skip rows with missing values.: Rows with missing values will not be used for the training.
  • Fail on observing missing values.: Learner node will fail during the execution.

Algorithm Settings

Alpha

Value added to the diagonal of the kernel matrix during fitting.

Kernel

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").

Available options:

  • Default: ConstantKernel(1.0, constant_value_bounds="fixed") * RBF(1.0, length_scale_bounds="fixed")
  • Constant Kernel: sklearn's ConstantKernel
  • Dot-Product Kernel: sklearn's DotProduct kernel
  • RBF: sklearn's Radial basis function kernel
  • White kernel: sklearn's WhiteKernel
Normalize the target column values

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.

Input Ports

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

Output Ports

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Trained Gaussian process regression model.

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Installation

To use this node in KNIME, install the extension KNIME Python Extension Development (Labs) from the below update site following our NodePit Product and Node Installation Guide:

v5.3

A zipped version of the software site can be downloaded here.

Plugin provider: KNIME AG, Zurich, Switzerland
Plugin version: 5.3.3.v202410291554
On NodePit since: 2024-07-09
Last update: 2024-11-21
KNIME versions: Since v5.2

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