The classification problem represents a funda-mental challenge in machine learning, with logistic regression serving as a traditional yet widely utilized method across various scientific disciplines.

In order to solve the problem of chronic heart failure risk prediction in the elderly, a logistic regression modeling framework with Bayesian method was proposed, aiming to solve the problem of ...

Our method, WLogit, consists in whitening the design matrix to remove the correlations between biomarkers, then using a penalized criterion adapted to the logistic regression model to select features.

Anthropic today released a new open source protocol to let all AI systems, not just its own, connect with data sources via a standard interface. Model Context Protocol (MCP), the company said in ...

Multivariate logistic regression analysis When cognitive frailty was used as the dependent variable (no = 0, yes = 1), 15 factors were found to be statistically significant in univariate analysis and ...

Want to understand logistic regression? Explore our guide to learn its applications and advantages in data analysis.

Logistic regression predicts the likelihood of an event happening, like whether someone voted or didn’t, based on a dataset of independent variables. This type of statistical model (also known as ...

According to a Bitcoin researcher, a quantile regression model indicates that there is a 99% percentile target for BTC at $275,000 by the end of November 2025.