FieldSelector

The FieldSelector algorithm uses the scikit-learn GenericUnivariateSelect to select the best predictor fields based on univariate statistical tests. For descriptions of the mode and param parameters, see the scikit-learn documentation at http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.GenericUnivariateSelect.html.

Parameters

The type parameter specifies if the field to predict is categorical or numeric.

Syntax

fit FieldSelector <field_to_predict> from <explanatory_fields>
[into <model name>] [type=<categorical, numeric>]
[mode=<k_best, fpr, fdr, fwe, percentile>] [param=<int>]

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

... | apply sla_model

You can inspect the model learned by FieldSelector with the summary command.

| summary sla_model

Example

The following example uses FieldSelector on a test set.

... | fit FieldSelector type=categorical SLA_violation from * into sla_model | ...

Local availability Permalink to this section#

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