LogisticRegression

The LogisticRegression algorithm uses the scikit-learn LogisticRegression estimator to fit a model to predict the value of categorical fields.

Parameters

• The fit_intercept parameter specifies whether the model includes an implicit intercept term.
• The default value of the fit_intercept parameter is True.
• The probabilities parameter specifies whether probabilities for each possible field value should be returned alongside the predicted value.
• The default value of the probabilities parameter is False.

Syntax

fit LogisticRegression <field_to_predict> from <explanatory_fields> [into <model name>]
[fit_intercept=<true|false>] [probabilities=<true|false>]

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

... | apply sla_model

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

... | summary sla_model

Example

The following examples uses LogisticRegression on a test set.

... | fit LogisticRegression SLA_violation from IO_wait_time into sla_model | ...

Local availability Permalink to this section#

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