Work

On the latent distribution of logistic regression — An empirical study on spectroscopic profiling datasets

Year: 2025

Type: article

Abstract: Logistic regression is a simple yet widely used classification model in spectroscopic profiling analysis. Considering the model’s output represents a probability, this paper will investigate its latent distribution assumption, i.e., its inner linear regressor unit follows a standard logistic distribution. An empirical study on five spectroscopic profiling open datasets, i.e., wine, coffee, olive oil, cheese, and milk powder, was conducted to verify this latent distribution assertion. This paper measured the GoF (Goodness of Fit) of each dataset’s latent variable from three aspects, i.e., curve fitting, P-P and Q-Q plots, and K-S test. After hyper-parameter optimization and proper training, the latent variable, as a weighted sum of the original features, has demonstrated a high level of GoF on all the five datasets. This study verifies the suitability of logistic regression in spectroscopic profiling analysis and answers why the model output can be interpreted as a conditional probability.

Source: Machine Learning with Applications

DOI: 10.1016/j.mlwa.2025.100712

Authors: Yinsheng Zhang, Mingming He, Haiyan Wang

Institution: Zhejiang Gongshang University

Language: English

Topic: Spectroscopy and Chemometric Analyses

Subfield: Analytical Chemistry

Field: Chemistry

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,340

Funders: National Natural Science Foundation of China, National Key Research and Development Program of China