Understanding Homoskedasticity in Regression Modeling With Examples

Discover the importance of homoskedasticity in regression models, where error variance is constant, and explore examples that illustrate this key concept.
The Lancet

When to and when not to use machine learning in risk prediction models

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...
Tech Xplore

Study finds 'dosed' nonlinearity can beat linear and fully nonlinear AI

Umbrella or sun cap? Buy or sell stocks? When it comes to questions like these, many people today rely on AI-supported recommendations. Chatbots such as ChatGPT, AI-driven weather forecasts, and ...
Devdiscourse

AI systems can create competing ‘algorithmic worlds’ from same data

AI & Society, states that algorithmic systems often construct competing but equally valid “model-worlds,” offering empirical support for a philosophical claim that evidence alone cannot uniquely ...