Dr. James McCaffrey of Microsoft Research demonstrates applying the L-BFGS optimization algorithm to the ML logistic regression technique for binary classification -- predicting one of two possible ...

While multiple machine learning (ML) algorithms offered similar predictive performance, the cost-effective analysis revealed ...

The sensitivity of the logistic regression algorithm was 76% and the specificity was 87% and was deemed more suitable for the classification of melanoma dermoscopic images over the support vector ...

The goal of binary classification is to predict a value that ... Other binary classifiers include SdcaLogisticRegression (logistic regression using a different optimization algorithm), LinearSvm ...

Which kind of algorithm works best (supervised, unsupervised, classification ... ranging in complexity from linear regression and logistic regression to deep neural networks and ensembles ...

We assessed the association of emergent classification of an ED visit ... We performed survey-weighted logistic regression analyses, adjusting for year and patient demographic and socioeconomic ...

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