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Explainable Random Forest

Explainable Random Forest (XRF) combines the predictive power of Breiman's Random Forest ensemble with systematic post-hoc attribution methods — principally SHAP values and mean-decrease-in-impurity importance — to make model decisions transparent and auditable. It delivers both high accuracy and human-interpretable feature contributions, satisfying demands from regulators, domain experts, and academic reviewers alike.

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Explainable Random Forest (Interpretable Ensemble with Feature Attribution)

Rekod kaedah taksonomik · ml-model / machine-learning

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