An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms

• URL: http://arxiv.org/abs/2412.04166v1
• Date: Thu, 05 Dec 2024 14:03:16 GMT
• Title: An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms
• Authors: Disha Ghandwani, Neeraj Sarna, Yuanyuan Li, Yang Lin,
• Abstract summary: We numerically analyze the performance of different methods in solving the risk-assessment problem. Our conformal prediction based approach is model and data-distribution agnostic, simple to implement, and provides reasonable results.
• Score: 10.008264048021076
• License:
• Abstract: Advanced classification algorithms are being increasingly used in safety-critical applications like health-care, engineering, etc. In such applications, miss-classifications made by ML algorithms can result in substantial financial or health-related losses. To better anticipate and prepare for such losses, the algorithm user seeks an estimate for the probability that the algorithm miss-classifies a sample. We refer to this task as the risk-assessment. For a variety of models and datasets, we numerically analyze the performance of different methods in solving the risk-assessment problem. We consider two solution strategies: a) calibration techniques that calibrate the output probabilities of classification models to provide accurate probability outputs; and b) a novel approach based upon the prediction interval generation technique of conformal prediction. Our conformal prediction based approach is model and data-distribution agnostic, simple to implement, and provides reasonable results for a variety of use-cases. We compare the different methods on a broad variety of models and datasets.

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