The rapid uptake of supervised machine learning (ML) in clinical prediction modelling, particularly for binary outcomes based on tabular data, has sparked debate about its comparative advantage over ...
Abstract: This research aims to compare the performance of Logistic Regression and Random Forest algorithms in classifying cyber-attack types. Using a data set consisting of 494,021 data points with ...
Abstract: Fuzzy classification models are important for handling uncertainty and heterogeneity in high-dimensional data. Although recent fuzzy logistic regression approaches have demonstrated ...
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