FACT: High-Dimensional Random Forests Inference. (arXiv:2207.01678v1 [stat.ML])

Random forests is one of the most widely used machine learning methods over
the past decade thanks to its outstanding empirical performance. Yet, because
of its black-box nature, the results by random forests can be hard to interpret
in many big data applications. Quantifying the usefulness of individual
features in random forests learning can greatly enhance its interpretability.
Existing studies have shown that some popularly used feature importance
measures for random forests suffer from the bias issue. In addition, there …

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