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 ...

Linear regression is the most fundamental machine learning technique to create a model that predicts a single numeric value. One of the three most common techniques to train a linear regression model ...

In previous article, we learned the magic of Propensity Score Matching (PSM) —how it finds "statistical twins" to mimic a randomized experiment. Now, it's time to open the toolbox and perform the ...

Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression using JavaScript. Linear regression is the simplest machine learning technique to predict a single numeric value, ...

This repository now supports Structured Linear Controlled Differential Equations (SLiCEs), which replace the non-linear vector fields of NCDEs and Log-NCDEs with structured linear vector fields, ...

Abstract: Anticipating geological formations prior to the advance of the drill bit is essential for optimizing drilling operations. This article introduces a novel design employing linear phased array ...

We present a gene-level regulatory model, single-cell ATAC + RNA linking (SCARlink), which predicts single-cell gene expression and links enhancers to target genes using multi-ome (scRNA-seq and ...

Back in August 2023, I started graduate school and weeks into my program I learned the power of linear regression. Since then, I have used it in most assignments and tasks. As a data enthusiast, I ...

Regularized regression analysis is a mature analytic approach to identify weighted sums of variables predicting outcomes. We present a novel Coarse Approximation Linear Function (CALF) to frugally ...

Abstract: In this paper, a new suboptimal modification for the log-MAP turbo decoding algorithm is proposed. This method (PL-log-MAP) based on the piecewise linear approximation of the the ...