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BorutaPy_API_Interface.md

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    BorutaPy_API_Interface["BorutaPy API Interface"]
    Feature_Selection_Core__fit_["Feature Selection Core (_fit)"]
    Feature_Transformation__transform_["Feature Transformation (_transform)"]
    Scikit_learn_Estimator_Wrapper["Scikit-learn Estimator Wrapper"]
    BorutaPy_API_Interface -- "initiates" --> Feature_Selection_Core__fit_
    BorutaPy_API_Interface -- "applies" --> Feature_Transformation__transform_
    BorutaPy_API_Interface -- "wraps" --> Scikit_learn_Estimator_Wrapper
    Feature_Selection_Core__fit_ -- "utilizes" --> Scikit_learn_Estimator_Wrapper
    Feature_Selection_Core__fit_ -- "provides results to" --> Feature_Transformation__transform_
    click BorutaPy_API_Interface href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/boruta_py/BorutaPy_API_Interface.md" "Details"
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The boruta_py subsystem provides a robust feature selection mechanism by wrapping a generic scikit-learn estimator. The BorutaPy API Interface serves as the primary entry point, exposing fit, transform, and fit_transform methods to users. Internally, the Feature Selection Core (_fit) component orchestrates the iterative Boruta algorithm, which involves generating shadow features, calculating feature importances using the wrapped Scikit-learn Estimator Wrapper, and statistically determining relevant features. Once the feature selection is complete, the Feature Transformation (_transform) component applies these results to the input data, returning a reduced dataset containing only the selected features. This architecture ensures a clear separation of concerns, with the API handling user interaction, the core component managing the algorithm, and the transformation component applying the results, all while leveraging the flexibility of scikit-learn estimators.

BorutaPy API Interface [Expand]

The primary user-facing component, handling initialization and exposing fit, transform, and fit_transform methods. It orchestrates the overall feature selection process. This component serves as the entry point for users interacting with the Boruta algorithm.

Related Classes/Methods:

Feature Selection Core (_fit)

An internal component responsible for executing the core Boruta algorithm. This involves the iterative process of evaluating feature importance, comparing it against shadow features, and refining the set of relevant features.

Related Classes/Methods:

Feature Transformation (_transform)

An internal component responsible for applying the results of the feature selection process. It takes the original dataset and returns a subset of features deemed relevant by the Boruta algorithm.

Related Classes/Methods:

Scikit-learn Estimator Wrapper

An external, generic scikit-learn compatible estimator (e.g., RandomForestClassifier) that the BorutaPy API Interface depends on and wraps. This estimator is used internally by the Boruta algorithm to assess feature importance.

Related Classes/Methods:

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