Scikit-learn: Difference between revisions

Revision as of 16:39, 19 March 2025

Revision as of 11:26, 20 March 2025

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* Consistent way of running machine learning models ({{code|estimator.fit()|python}} and {{code|estimator.predict()|python}}), which libraries can implement

* Declarative way of structuring a data science process (the {{Code|Pipeline|Python}}), including data pre-processing and model fitting

== Examples ==

Fitting a [[Random forest|random forest classifier]]:<syntaxhighlight lang="python3" line="1">

>>> from sklearn.ensemble import RandomForestClassifier

>>> classifier = RandomForestClassifier(random_state=0)

>>> X = [[ 1, 2, 3], # 2 samples, 3 features

... [11, 12, 13]]

>>> y = [0, 1] # classes of each sample

>>> classifier.fit(X, y)

RandomForestClassifier(random_state=0)

</syntaxhighlight>

==Implementation==

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* [[Infer.NET]]

* [[List of numerical analysis software]]

==References==

{{Reflist|30em}}