Large Deviations for Classification Performance Analysis of Machine Learning Systems

Autor:	Braca, Paolo, Millefiori, Leonardo M., Aubry, Augusto, De Maio, Antonio, Willett, Peter
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Electrical Engineering and Systems Science - Signal Processing Mathematics - Probability Statistics - Applications Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	We study the performance of machine learning binary classification techniques in terms of error probabilities. The statistical test is based on the Data-Driven Decision Function (D3F), learned in the training phase, i.e., what is thresholded before the final binary decision is made. Based on large deviations theory, we show that under appropriate conditions the classification error probabilities vanish exponentially, as $\sim \exp\left(-n\,I + o(n) \right)$, where $I$ is the error rate and $n$ is the number of observations available for testing. We also propose two different approximations for the error probability curves, one based on a refined asymptotic formula (often referred to as exact asymptotics), and another one based on the central limit theorem. The theoretical findings are finally tested using the popular MNIST dataset. Comment: 5 pages, 3 figures, 1 table
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2301.07104 Zobrazit plný text záznamu View this record from Arxiv