When training a logistic regression model, there are many optimization algorithms that can be used, such as stochastic gradient descent (SGD), iterated Newton-Raphson, Nelder-Mead and L-BFGS. This ...

Spanish researchers have developed a new mathematical model that predicts sport injuries. Their work shows that sport injuries that affect the lower limbs in high-impact sport, such as football ...

What are the advantages of logistic regression over decision trees? ... No algorithm is in general ‘better’ than another. There is the famous “No Free Lunch” theorem.

Logistic regression can be applied in customer service, when you examine historical data on purchasing behaviour to personalise offerings. The afterword We’ve touched upon three common models of ...

Logistic regression analysis of high-dimensional data, such as natural language text, poses computational and statistical challenges. Maximum likelihood estimation often fails in these applications.

EHR data may be particularly suitable for machine learning (ML) techniques, as such algorithms can process high-dimensional data and capture nonlinear relationships between variables. By comparison, ...

A Comparison of Logistic Regression Against Machine Learning Algorithms for Gastric Cancer Risk Prediction Within Real-World Clinical Data Streams. JCO Clin Cancer Inform 6 , e2200039 (2022). DOI: ...