Unbiased Recursive Partitioning: A Conditional Inference Framework

Abstract¶

A unified framework for recursive partitioning is proposed which embeds tree-structured regression models into a well defined theory of conditional inference procedures. The approach results in unbiased variable selection and does not require additional pruning. The stopping criterion is based on multiple test procedures. The framework is applicable to all kinds of regression problems including nominal, ordinal, numeric, censored as well as multivariate response variables and arbitrary measurement scales of the covariates.

Summary¶

Introduces a conditional inference framework for decision trees that uses statistical tests to select split variables and stopping points. The approach reduces selection bias toward high-cardinality predictors and removes the need for a separate pruning stage.

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