In this article, we propose using the principle of boosting to reduce the bias of a random forest prediction in the regression setting. From the original random forest fit, we extract the residuals an
We present a massively parallel algorithm for the fused lasso, powered by a multiple number of graphics processing units (GPUs). Our method is suitable for a class of large-scale sparse regression pro
OR corresponds to odds ratio, *Adjusted for age, gender, hypertension, hypercholesterolaemia, diabetes and Charlson Index. **equates to statistical significance. ***Time-dependent parameterization of
This dataset provides the experimental and numerical data supporting the results presented in the manuscript “An Experimental–Numerical Framework for Springback Prediction and Angle Compensation in Ai
