GradientBoostingRegressor

This algorithm uses the GradientBoostingRegressor algorithm from scikit-learn to build a regression model by fitting regression trees on the negative gradient of a loss function. The kfold cross-validation command can be used with GradientBoostingRegressor. See, K-fold_cross-validation.

For further information see the scikit-learn documentation at http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html

Syntax

fit GradientBoostingRegressor <field_to_predict> from <explanatory_fields>
[into <model_name>] [loss=<ls|lad|huber|quantile>]
[max_features=<str>] [learning_rate=<float>] [min_weight_fraction_leaf=<float>]
[alpha=<float>] [subsample=<float>] [n_estimators=<int>] [max_depth=<int>]
[min_samples_split=<int>] [min_samples_leaf=<int>] [max_leaf_nodes=<int>]
[random_state=<int>]

You can use the apply method to apply the trained model to the new data.

...|apply temperature_model

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

... | summary temperature_model

Example

The following example uses the GradientBoostingRegressor algorithm to fit a model and saves that model as temperature_model.

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

Local availability Permalink to this section#

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