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 ...

Predicting performance for large-scale industrial systems—like Google’s Borg compute clusters—has traditionally required extensive domain-specific feature engineering and tabular data representations, ...

Multivariable regression models are a workhorse in clinical-epidemiological research to associate exposures with outcomes, to adjust for confounding, and assess effect-measure modification. They are ...

ABSTRACT: In this work, we seek the relationship between the order of the polynomial model and the number of knots and intervals that we need to fit the splines regression model. Regression models ...

A Bitcoin researcher says a quasi-exponential decay trend could even see BTC's price going as high as $300,000 in 2025. After dropping 3.45% on Sept. 30, Bitcoin (BTC) missed out on a monthly bullish ...

Background: The Centre for Disease Control and Prevention in Yangquan, China, has taken a series of preventive and control measures in response to the increasing trend of Kala-Azar. In response, we ...

The majority of research predicted heating demand using linear regression models, but they did not give current building features enough context. Model problems such as Multicollinearity need to be ...

Autistic regression refers to a loss of previously acquired skills or a backtracking of developmental milestones. In young children, it may represent autism onset. In older children and adults, it may ...