Abstract: Logistic regression is widely used for binary classification; however, its performance in real-world applications is often hindered by multicollinearity, high-dimensional feature spaces, and ...

c-lasso is a Python package that enables sparse and robust linear regression and classification with linear equality constraints on the model parameters. For detailed info, one can check the ...

Are Machine Learning (ML) algorithms superior to traditional econometric models for GDP nowcasting in a time series setting? Based on our evaluation of all models from both classes ever used in ...

Adaptive Lasso is an extension of the standard Lasso method that provides improved feature selection properties through weighted L1 penalties. It assigns different weights to different coefficients in ...

Although an intracranial aneurysm (IA) is widespread and fatal, few drugs can be used to prevent its rupture. This study explored the molecular mechanism and potential targets of IA rupture through ...

Abstract: This paper is a novel approach to improving the accuracy of wind power generation predictions by using linear regression (LR) algorithm differentiated with the Lasso regression (LaR). The ...

The rise of artificial intelligence (AI) deep learning algorithms is helping to accelerate brain-computer interfaces (BCIs). Published in this month’s Nature Neuroscience is new research that shows ...

In the subject of machine learning, it is essential to comprehend regression algorithms. Ten fundamental regression algorithms are introduced in this tutorial, which serves as the foundation for many ...