Date of Award
5-2025
Degree Type
Report
Degree Name
Master of Science (MS)
Department
Mathematics and Statistics
Committee Chair(s)
John R. Stevens (Committee Chair)
Committee
John R. Stevens
Committee
Luis Gordillo
Committee
Brennan Bean
Abstract
Machine learning models can take a collection of inputs and craft an output. The mathematical formulas these models use to calculate their outputs easily become too complex or time consuming for a human to analyze. Collectively, we refer to these as black box models. Accumulated local effects plots (ALE) are a method for adding interpretability and visibility into the effects that individual variables contribute to the predictions made by black box models. The method designed by D.W. Apley calculates equally spaced point estimates of the response value to construct a graph across the range of the variable of interest. AleCI improves Apley’s original implementation by adding a confidence interval (CI) around each prediction showing the range where the true value of this estimator should exist. AleCI uses bagged models to bootstrap the value at each point to create a nonparametric confidence interval (default) or an interval using a user provided function. Furthermore, AleCI updates the graphing capabilities of the original by implementing ggplot2 as the default mechanism.
Recommended Citation
Lister, Matthew R., "AleCI: An R Package For Non-Parametric Confidence Intervals On Accumulated Local Effects Plots" (2025). All Graduate Reports and Creative Projects, Fall 2023 to Present. 79.
https://digitalcommons.usu.edu/gradreports2023/79
Included in
Categorical Data Analysis Commons, Data Science Commons, Multivariate Analysis Commons, Probability Commons, Statistical Models Commons
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