TY - JOUR AU - Debeer, Dries AU - Strobl, Carolin PY - 2020 DA - 2020/07/14 TI - Conditional permutation importance revisited JO - BMC Bioinformatics SP - 307 VL - 21 IS - 1 AB - Random forest based variable importance measures have become popular tools for assessing the contributions of the predictor variables in a fitted random forest. In this article we reconsider a frequently used variable importance measure, the Conditional Permutation Importance (CPI). We argue and illustrate that the CPI corresponds to a more partial quantification of variable importance and suggest several improvements in its methodology and implementation that enhance its practical value. In addition, we introduce the threshold value in the CPI algorithm as a parameter that can make the CPI more partial or more marginal. SN - 1471-2105 UR - https://doi.org/10.1186/s12859-020-03622-2 DO - 10.1186/s12859-020-03622-2 ID - Debeer2020 ER -