The random forest algorithm, proposed by L. Breiman in 2001, has been extremely
successful as a general-purpose classification and regression method. The approach, which …

This paper reviews and advocates against the use of permute-and-predict (PaP) methods for
interpreting black box functions. Methods such as the variable importance measures …

Random forests remain among the most popular off-the-shelf supervised machine learning
tools with a well-established track record of predictive accuracy in both regression and …

Hypothesis testing of variable importance measures (VIMPs) is still the subject of ongoing
research. This particularly applies to random forests (RF), for which VIMPs are a popular …

Ensembles of decision trees perform well on many problems, but are not interpretable. In
contrast to existing approaches in interpretability that focus on explaining relationships …

Spatiotemporal predictions of bycatch (ie, catch of nontargeted species) have shown
promise as dynamic ocean management tools for reducing bycatch. However, which …

In this article, we propose using the principle of boosting to reduce the bias of a random
forest prediction in the regression setting. From the original random forest fit, we extract the …

In observational demographic data, the number of measured factors that could potentially
drive demography (such as daily weather records between two censuses) can easily exceed …

Global feature effect methods, such as partial dependence plots, provide an intelligible
visualization of the expected marginal feature effect. However, such global feature effect …

Despite the number of problems that can occur when core model assumptions are violated,
nearly all quantitative political science research relies on inflexible regression models that …