The core of big data models lies in the synergy of algorithm innovation, computational power support, and data governance to ...

Logistic regression is the most cost-effective model for medial vascular calcification classification, with a mean ICER of $278 using five low-cost features. Despite similar diagnostic accuracy ...

Binary Classification and Regression in Machine Learning Overview This project focuses on implementing machine learning models for two key tasks: Binary Classification: Predicting categorical outcomes ...

This study demonstrates that multinomial logistic regression with a ridge estimator shows significant classification accuracy (p < 0.05) and effectively detects pilot mental workload in real flight ...

This repository compares the performance of Adaline, Logistic Regression, and Perceptron models on binary classification tasks using linearly, non-linearly, and marginally separable datasets from the ...

We present a new model for multivariate time-series classification, called the hidden-unit logistic model (HULM), that uses binary stochastic hidden units to model latent structure in the data. The ...

The purpose of this work is to investigate the effectiveness of DensNet and Logistic Regression in terms of accurately predicting the classification of footwear trends.In this study, there are two ...

Metrics such as accuracy, precision, recall, and F 1 score provide different perspectives on model performance, and using appropriate validation methods like cross-validation can give a more reliable ...