5 papers
Conditional Local Importance by Quantile Expectations
Kelvyn K. Bladen, Adele Cutler, D. Richard Cutler +1
Global variable importance measures are commonly used to interpret the results of machine learning models. Local variable importance techniques assess how variables contribute to i…
The Generalized Proximity Forest
Ben Shaw, Adam Rustad, Sofia Pelagalli Maia +2
Recent work has demonstrated the utility of Random Forest (RF) proximities for various supervised machine learning tasks, including outlier detection, missing data imputation, and…
Continuous Symmetry Discovery and Enforcement Using Infinitesimal Generators of Multi-parameter Group Actions
Ben Shaw, Sasidhar Kunapuli, Abram Magner +1
Symmetry-informed machine learning can exhibit advantages over machine learning which fails to account for symmetry. In the context of continuous symmetry detection, current state…
Forest Proximities for Time Series
Ben Shaw, Jake Rhodes, Soukaina Filali Boubrahimi +1
RF-GAP has recently been introduced as an improved random forest proximity measure. In this paper, we present PF-GAP, an extension of RF-GAP proximities to proximity forests, an ac…
Symmetry Discovery Beyond Affine Transformations
Ben Shaw, Abram Magner, Kevin R. Moon
Symmetry detection can improve various machine learning tasks. In the context of continuous symmetry detection, current state of the art experiments are limited to detecting affine…