论逻辑回归的潜在分布——光谱分析数据集的实证研究

IF 4.9
Yinsheng Zhang, Mingming He, Haiyan Wang
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引用次数: 0

摘要

逻辑回归是一种简单而广泛应用于光谱分析的分类模型。考虑到模型的输出代表一个概率,本文将研究其潜在分布假设,即其内部线性回归量单元服从标准logistic分布。对葡萄酒、咖啡、橄榄油、奶酪和奶粉等5个光谱分析开放数据集进行了实证研究,验证了这一潜在分布断言。本文从曲线拟合、P-P和Q-Q图以及K-S检验三个方面对每个数据集潜在变量的GoF(拟合优度)进行了测量。经过超参数优化和适当的训练,潜变量作为原始特征的加权和,在所有5个数据集上都表现出较高的GoF水平。本研究验证了逻辑回归在光谱剖面分析中的适用性,并回答了为什么模型输出可以解释为条件概率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On the latent distribution of logistic regression — An empirical study on spectroscopic profiling datasets
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.
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来源期刊
Machine learning with applications
Machine learning with applications Management Science and Operations Research, Artificial Intelligence, Computer Science Applications
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