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Interpretable Model-Aware Counterfactual Explanations for Random Forest
Authors:
Joshua S. Harvey,
Guanchao Feng,
Sai Anusha Meesala,
Tina Zhao,
Dhagash Mehta
Abstract:
Despite their enormous predictive power, machine learning models are often unsuitable for applications in regulated industries such as finance, due to their limited capacity to provide explanations. While model-agnostic frameworks such as Shapley values have proved to be convenient and popular, they rarely align with the kinds of causal explanations that are typically sought after. Counterfactual…
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Despite their enormous predictive power, machine learning models are often unsuitable for applications in regulated industries such as finance, due to their limited capacity to provide explanations. While model-agnostic frameworks such as Shapley values have proved to be convenient and popular, they rarely align with the kinds of causal explanations that are typically sought after. Counterfactual case-based explanations, where an individual is informed of which circumstances would need to be different to cause a change in outcome, may be more intuitive and actionable. However, finding appropriate counterfactual cases is an open challenge, as is interpreting which features are most critical for the change in outcome. Here, we pose the question of counterfactual search and interpretation in terms of similarity learning, exploiting the representation learned by the random forest predictive model itself. Once a counterfactual is found, the feature importance of the explanation is computed as a function of which random forest partitions are crossed in order to reach it from the original instance. We demonstrate this method on both the MNIST hand-drawn digit dataset and the German credit dataset, finding that it generates explanations that are sparser and more useful than Shapley values.
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Submitted 31 October, 2025;
originally announced October 2025.
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Explainable Unsupervised Anomaly Detection with Random Forest
Authors:
Joshua S. Harvey,
Joshua Rosaler,
Mingshu Li,
Dhruv Desai,
Dhagash Mehta
Abstract:
We describe the use of an unsupervised Random Forest for similarity learning and improved unsupervised anomaly detection. By training a Random Forest to discriminate between real data and synthetic data sampled from a uniform distribution over the real data bounds, a distance measure is obtained that anisometrically transforms the data, expanding distances at the boundary of the data manifold. We…
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We describe the use of an unsupervised Random Forest for similarity learning and improved unsupervised anomaly detection. By training a Random Forest to discriminate between real data and synthetic data sampled from a uniform distribution over the real data bounds, a distance measure is obtained that anisometrically transforms the data, expanding distances at the boundary of the data manifold. We show that using distances recovered from this transformation improves the accuracy of unsupervised anomaly detection, compared to other commonly used detectors, demonstrated over a large number of benchmark datasets. As well as improved performance, this method has advantages over other unsupervised anomaly detection methods, including minimal requirements for data preprocessing, native handling of missing data, and potential for visualizations. By relating outlier scores to partitions of the Random Forest, we develop a method for locally explainable anomaly predictions in terms of feature importance.
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Submitted 22 April, 2025;
originally announced April 2025.
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Pressure-induced suppression of ferromagnetism in the itinerant ferromagnet LaCrSb$_3$
Authors:
Z. E. Brubaker,
J. S. Harvey,
J. R. Badger,
R. R. Ullah,
D. J. Campbell,
Y. Xiao,
P. Chow,
C. Kenney-Benson,
J. S. Smith,
C. Reynolds,
J. Paglione,
R. J. Zieve,
J. R. Jeffries,
V. Taufour
Abstract:
We have performed an extensive pressure-dependent structural, spectroscopic, and electrical transport study of LaCrSb$_3$. The ferromagnetic phase (T$_C$ = 120 K at p = 0 GPa) is fully suppressed by p = 26.5 GPa and the Cr-moment decreases steadily with increasing pressure. The unit cell volume decreases smoothly up to p = 55 GPa. We find that the bulk modulus and suppression of the magnetism are…
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We have performed an extensive pressure-dependent structural, spectroscopic, and electrical transport study of LaCrSb$_3$. The ferromagnetic phase (T$_C$ = 120 K at p = 0 GPa) is fully suppressed by p = 26.5 GPa and the Cr-moment decreases steadily with increasing pressure. The unit cell volume decreases smoothly up to p = 55 GPa. We find that the bulk modulus and suppression of the magnetism are in good agreement with theoretical predictions, but the Cr-moment decreases smoothly with pressure, in contrast to steplike drops predicted by theory. The ferromagnetic ordering temperature appears to be driven by the Cr-moment.
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Submitted 22 May, 2020;
originally announced May 2020.
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Magnetic fluctuations in the itinerant ferromagnet LaCrGe3 studied by 139La NMR
Authors:
K. Rana,
H. Kotegawa,
R. R. Ullah,
J. S. Harvey,
S. L. Bud'ko,
P. C. Canfield,
H. Tou,
V. Taufour,
Y. Furukawa
Abstract:
LaCrGe$_3$ is an itinerant ferromagnet with a Curie temperature of $T_{\rm c}$ = 85 K and exhibits an avoided ferromagnetic quantum critical point under pressure through a modulated antiferromagnetic phase as well as tri-critical wing structure in its temperature-pressure-magnetic field ($T$-$p$-$H$) phase diagram. In order to understand the static and dynamical magnetic properties of LaCrGe$_3$,…
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LaCrGe$_3$ is an itinerant ferromagnet with a Curie temperature of $T_{\rm c}$ = 85 K and exhibits an avoided ferromagnetic quantum critical point under pressure through a modulated antiferromagnetic phase as well as tri-critical wing structure in its temperature-pressure-magnetic field ($T$-$p$-$H$) phase diagram. In order to understand the static and dynamical magnetic properties of LaCrGe$_3$, we carried out $^{139}$La nuclear magnetic resonance (NMR) measurements. Based on the analysis of NMR data, using the self-consistent-renomalization (SCR) theory, the spin fluctuations in the paramagnetic state are revealed to be isotropic ferromagnetic and three dimensional (3D) in nature. Moreover, the system is found to follow the generalized Rhodes-Wohfarth relation which is expected in 3D itinerant ferromagnetic systems. As compared to other similar itinerant ferromagnets, the Cr 3$d$ electrons and their spin fluctuations are characterized to have a relatively high degree of localization in real space.
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Submitted 1 June, 2019;
originally announced June 2019.
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Overt visual attention on rendered 3D objects
Authors:
Oleksii Sidorov,
Joshua S. Harvey,
Hannah E. Smithson,
Jon Y. Hardeberg
Abstract:
This work covers multiple aspects of overt visual attention on 3D renders: measurement, projection, visualization, and application to studying the influence of material appearance on looking behaviour. In the scope of this work, we ran an eye-tracking experiment in which the observers are presented with animations of rotating 3D objects. The objects were rendered to simulate different metallic app…
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This work covers multiple aspects of overt visual attention on 3D renders: measurement, projection, visualization, and application to studying the influence of material appearance on looking behaviour. In the scope of this work, we ran an eye-tracking experiment in which the observers are presented with animations of rotating 3D objects. The objects were rendered to simulate different metallic appearance, particularly smooth (glossy), rough (matte), and coated gold. The eye-tracking results illustrate how material appearance itself influences the observer's attention, while all the other parameters remain unchanged. In order to make visualization of the attention maps more natural and also make the analysis more accurate, we develop a novel technique of projection of gaze fixations on the 3D surface of the figure itself, instead of the conventional 2D plane of the screen. The proposed methodology will be useful for further studies of attention and saliency in the computer graphics domain.
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Submitted 24 May, 2019;
originally announced May 2019.