Tree-based machine learning models such as random forests, decision trees and gradient
boosted trees are popular nonlinear predictive models, yet comparatively little attention has …

Tree-based machine learning models such as random forests, decision trees, and gradient
boosted trees are the most popular non-linear predictive models used in practice today, yet …

Abstract Model interpretability has become an important problem in machine learning (ML)
due to the increased effect algorithmic decisions have on humans. Counterfactual …

Interpretability has become incredibly important as machine learning is increasingly used to
inform consequential decisions. We propose to construct global explanations of complex …

The ability to interpret machine learning models has become increasingly important now that
machine learning is used to inform consequential decisions. We propose an approach …

The expansion of machine learning to high-stakes application domains such as medicine,
finance, and criminal justice, where making informed decisions requires clear understanding …

Counterfactual explanations are usually generated through heuristics that are sensitive to
the search's initial conditions. The absence of guarantees of performance and robustness …

Interpreting state-of-the-art machine learning algorithms can be difficult. For example, why
does a complex ensemble predict a particular class? Existing approaches to interpretable …

Modern machine learning methods typically produce “black box” models that are opaque to
interpretation. Yet, their demand has been increasing in the Human-in-the-Loop processes …

Abstract Model interpretability is an increasingly important component of practical machine
learning. Some of the most common forms of interpretability systems are example-based …