GradientBoostingClassifier

This algorithm uses the GradientBoostingClassifier from scikit-learn to build a classification model by fitting regression trees on the negative gradient of a deviance loss function. For further information, see the sci-kit learn documentation: http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html.

Syntax

fit GradientBoostingClassifier <field_to_predict> from <explanatory_fields>[into <model name>]
[loss=<deviance | exponential>] [max_features=<str>]
[learning_rate =<float>] [min_weight_fraction_leaf=<float>] [n_estimators=<int>]
[max_depth=<int>] [min_samples_split =<int>] [min_samples_leaf=<int>]
[max_leaf_nodes=<int>] [random_state=<int>]

You can apply the saved model later to new data using the apply command.

... | apply TESTMODEL_GradientBoostingClassifier

You can inspect features learned by GradientBoostingClassifier with the summary command.

... | summary TESTMODEL_GradientBoostingClassifier

Example

The following example uses GradientBoostingClassifier on a test set.

... | fit GradientBoostingClassifier target from * into TESTMODEL_GradientBoostingClassifier

Local availability Permalink to this section#

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