As well as this, they have identified the most effective features for examining COVID-19 data with linear regression models, which should be of help to bioinformaticians studying datasets where ...

The most successful algorithm, the neural network, actually was correct 7.6 percent more often than the ACC/AHA method, and resulted in 1.6 percent fewer false positives.

An algorithm is presented for nonlinear least squares estimation in which the parameters to be estimated can be regarded as all nonlinear (the traditional approach) or reclassified as linear-nonlinear ...

An efficient branch-and-bound algorithm for computing the best-subset regression models is proposed. The algorithm avoids the computation of the whole regression tree that generates all possible ...

My 2019 TechSEO Boost presentation. Michael King’s Runtime video. See Hulya Coban ‘s article for how to write a regression study as well as use Python to run a linear regression model.

Instructor Fall 2016: Sriram SankaranarayananPrerequisitesCalculus I,II + Algorithms + Linear Algebra.Topics CoveredRoughly, we will cover the following topics (some of them may be skipped depending ...

I use Python 3 and Jupyter Notebooks to generate plots and equations with linear regression on Kaggle data. I checked the correlations and built a basic machine learning model with this dataset.

Variable imputation was performed by polytomous regression (unordered categorical variables), LR (binary variables), and Bayesian linear regression (continuous variables). Multiple (m = 5) imputation ...