DecisionTreeClassifier

The DecisionTreeClassifier algorithm uses the scikit-learn DecisionTreeClassifier estimator to fit a model to predict the value of categorical fields. For further information, see the sci-kit learn documentation: http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html.

Parameters

To specify the maximum depth of the tree to summarize, use the limit argument. The default value for the limit argument is 5.

... | summary model_DTC limit=10

Syntax

fit DecisionTreeClassifier <field_to_predict> from <explanatory_fields> [into <model_name>]
[max_depth=<int>] [max_features=<str>] [min_samples_split=<int>] [max_leaf_nodes=<int>]
[criterion=<gini|entropy>] [splitter=<best|random>] [random_state=<int>]

You can save DecisionTreeClassifier models by using the into keyword and apply it to new data later by using the apply command.

... | apply model_DTC

You can inspect the decision tree learned by DecisionTreeClassifier with the summary command.

... | summary model_DTC

See a JSON representation of the tree by giving json=t as an argument to the summary command.

... | summary model_DTC json=t

Example

The following example uses DecisionTreeClassifier on a test set.

... | fit DecisionTreeClassifier SLA_violation from * into sla_model | ...

Local availability Permalink to this section#

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