Ridge

The Ridge algorithm uses the scikit-learn Ridge estimator to fit a model to predict the value of numeric fields. Ridge is like LinearRegression, but it uses L2 regularization to learn a linear models with smaller coefficients, making the algorithm more robust to collinearity. The kfold cross-validation command can be used with Ridge. See, K-fold_cross-validation.

For descriptions of the fit_intercept, normalize, and alpha parameters, see the scikit-learn documentation at http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html.

Parameters

The alpha parameter specifies the degree of regularization. The default value is 1.0.

Syntax

fit Ridge <field_to_predict> from <explanatory_fields>
[into <model name>] [fit_intercept=<true|false>] [normalize=<true|false>]
[alpha=<int>]

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

... | apply temperature_model

You can inspect the coefficients learned by Ridge with the summary command.

... | summary temperature_model

Example

The following example uses Ridge on a test set.

... | fit Ridge temperature from date_month date_hour normalize=true alpha=0.5 | ...

Local availability Permalink to this section#

• Local class: Ridge
• Source file: Splunk_ML_Toolkit/bin/algos/Ridge.py (in-repo path Splunk_ML_Toolkit/bin/algos/Ridge.py)
• algos.conf stanza: [Ridge]
• 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).