KernelRidge

The KernelRidge algorithm uses the scikit-learn KernelRidge algorithm to fit a model to predict numeric fields. This algorithm uses the radial basis function (rbf) kernel by default. The kfold cross-validation command can be used with KernelRidge. See, K-fold_cross-validation.

For details, see the scikit-learn documentation at http://scikit-learn.org/stable/modules/generated/sklearn.kernel_ridge.KernelRidge.html.

Parameters

The gamma parameter controls the width of the rbf kernel. The default value is 1/ number of fields.

Syntax

fit KernelRidge <field_to_predict> from <explanatory_fields> [into <model_name>] [gamma=<float>]

You can save KernelRidge models using the into keyword and apply new data later using the apply command.

... | apply sla_model

Syntax constraints

You cannot inspect the model learned by KernelRidge with the summary command.

Example

The following example uses KernelRidge on a test set.

... | fit KernelRidge temperature from date_month date_hour into temperature_model

Local availability Permalink to this section#

• Local class: KernelRidge
• Source file: Splunk_ML_Toolkit/bin/algos/KernelRidge.py (in-repo path Splunk_ML_Toolkit/bin/algos/KernelRidge.py)
• algos.conf stanza: [KernelRidge]
• Class bases: RegressorMixin, BaseAlgo

Source Permalink to this section#

Adapted from the Splunk AI Toolkit 5.6.4 documentation at /en/splunk-cloud-platform/apply-machine-learning/use-ai-toolkit/5.6.4/algorithms-and-scoring-metrics-in-the-ai-toolkit/algorithms-in-the-ai-toolkit (section: regressor).