This issue has now been addressed. Li Hang's newly launched book 'Machine Learning Methods (2nd Edition)' dedicates a chapter ...

The development of every field relies on a few foundational classic books, and artificial intelligence is no exception.

Then we discuss some specific methods from the ML literature that we view as important for empirical researchers in economics. These include supervised learning methods for regression and ...

Linear regression, multivariate regression, subset selection, shrinkage methods, principal component regression, and partial least squares. Decision trees, regression trees, stopping criterion and ...

Logistic regression outperformed ML methods in 4 out of 12 studies. Three studies reported model performance of AUC>0.9, yet the clinical relevance is questionable. Conclusions A variety of different ...

This video is a one stop shop for understanding What is linear regression in machine learning. Linear regression in machine learning is considered as the basis or foundation in machine learning ...

Gynecological cancers, including breast, ovarian, and cervical malignancies, account for a significant global health burden among women. The review outlines how a spectrum of machine learning (ML) ...

The BO-GBRT model accurately predicts compressive strength in self-compacting concrete with recycled aggregates, improving upon traditional testing methods.