To cite tree.interpreter in publications use:

Li X, Wang Y, Basu S, Kumbier K, Yu B (2019). “A Debiased MDI Feature Importance Measure for Random Forests.” arXiv:1906.10845 [cs, stat]. arXiv: 1906.10845, http://arxiv.org/abs/1906.10845.

Corresponding BibTeX entry:

  @Article{,
    title = {A {Debiased} {MDI} {Feature} {Importance} {Measure} for
      {Random} {Forests}},
    url = {http://arxiv.org/abs/1906.10845},
    abstract = {Tree ensembles such as Random Forests have achieved
      impressive empirical success across a wide variety of
      applications. To understand how these models make predictions,
      people routinely turn to feature importance measures calculated
      from tree ensembles. It has long been known that Mean Decrease
      Impurity (MDI), one of the most widely used measures of feature
      importance, incorrectly assigns high importance to noisy
      features, leading to systematic bias in feature selection. In
      this paper, we address the feature selection bias of MDI from
      both theoretical and methodological perspectives. Based on the
      original definition of MDI by Breiman et al. for a single tree,
      we derive a tight non-asymptotic bound on the expected bias of
      MDI importance of noisy features, showing that deep trees have
      higher (expected) feature selection bias than shallow ones.
      However, it is not clear how to reduce the bias of MDI using its
      existing analytical expression. We derive a new analytical
      expression for MDI, and based on this new expression, we are able
      to propose a debiased MDI feature importance measure using
      out-of-bag samples, called MDI-oob. For both the simulated data
      and a genomic ChIP dataset, MDI-oob achieves state-of-the-art
      performance in feature selection from Random Forests for both
      deep and shallow trees.},
    urldate = {2019-10-18},
    journal = {arXiv:1906.10845 [cs, stat]},
    author = {Xiao Li and Yu Wang and Sumanta Basu and Karl Kumbier and
      Bin Yu},
    month = {jun},
    year = {2019},
    note = {arXiv: 1906.10845},
    keywords = {Statistics - Machine Learning, Computer Science -
      Machine Learning},
    annote = {Comment: The first two authors contributed equally to
      this paper},
  }