AUCμ: A Performance Metric for Multi-Class Machine Learning Models
Authors: Ross Kleiman, David Page
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide in this work a multi-class extension of AUC that we call AUCµ that is derived from first principles of the binary class AUC. AUCµ has similar computational complexity to AUC and maintains the properties of AUC critical to its interpretation and use. |
| Researcher Affiliation | Academia | 1Department of Computer Sciences, University of Wisconsin Madison, Madison, Wisconsin 2Department of Biostatistics and Medical Informatics, University of Wisconsin Madison, Madison, Wisconsin. |
| Pseudocode | No | The paper describes its derivation and formulas (e.g., Equation 4 for AUCµ), but it does not contain any structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for the described methodology, nor does it include any links to a code repository. |
| Open Datasets | No | The paper is theoretical in nature and does not describe the use of any datasets for training or evaluation, and thus provides no concrete access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) as it focuses on theoretical analysis rather than empirical evaluation with datasets. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or detailed computer specifications) used for running experiments, as the paper is theoretical and does not describe empirical experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate experiments, as it is a theoretical paper without reported empirical results requiring such dependencies. |
| Experiment Setup | No | The paper does not contain specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) as it focuses on theoretical contributions rather than empirical experimentation. |