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 ...

But there is also some empirical work comparing various algorithms across many datasets and drawing some conclusions, what types of problems tend to do better with trees vs logistic regression.

The Data Science Lab. Logistic Regression with Batch SGD Training and Weight Decay Using C#. Dr. James McCaffrey from Microsoft Research presents a complete end-to-end program that explains how to ...

There are dozens of machine learning algorithms, ranging in complexity from linear regression and logistic regression to deep neural networks and ensembles (combinations of other models). However ...

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 ...

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, ...

Citation: Regression approach outperforms ML algorithms in predicting optimal surgical method in submucosal tumor patients (2024, February 28) retrieved 15 May 2025 from https://medicalxpress.com ...

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: ...