83 citations · 224 across the 27 of their papers we have counts for
4 papers · 2 filters
Using Model-Based Trees with Boosting to Fit Low-Order Functional ANOVA Models
Linwei Hu, Jie Chen, Vijayan N. Nair
Low-order functional ANOVA (fANOVA) models have been rediscovered in the machine learning (ML) community under the guise of inherently interpretable machine learning. Explainable B…
Quantifying Inherent Randomness in Machine Learning Algorithms
Soham Raste, Rahul Singh, Joel Vaughan +1
Most machine learning (ML) algorithms have several stochastic elements, and their performances are affected by these sources of randomness. This paper uses an empirical study to sy…
Performance and Interpretability Comparisons of Supervised Machine Learning Algorithms: An Empirical Study
Alice J. Liu, Arpita Mukherjee, Linwei Hu +2
This paper compares the performances of three supervised machine learning algorithms in terms of predictive ability and model interpretation on structured or tabular data. The algo…
Explaining Adverse Actions in Credit Decisions Using Shapley Decomposition
Vijayan N. Nair, Tianshu Feng, Linwei Hu +3
When a financial institution declines an application for credit, an adverse action (AA) is said to occur. The applicant is then entitled to an explanation for the negative decision…