There are dozens of code libraries and tools that can create a logistic regression prediction model, including Keras, scikit-learn, Weka and PyTorch. When training a logistic regression model, there ...

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.

What is regression? A regression model is an algorithm that tries to fit itself to the presented data best. In essence, it is a line of best fit.

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.

As defined on TechTarget, logistic regression is a statistical analysis method used to predict a data value based on prior observations of a data set. A logistic regression model predicts a ...

Tomohiro Ando, Ker-Chau Li, A Model-Averaging Approach for High-Dimensional Regression, Journal of the American Statistical Association, Vol. 109, No. 505 (March 2014), pp. 254-265 ...

A sequential regression or chained equations imputation approach uses a Gibbs sampling-type iterative algorithm that imputes the missing values using a sequence of conditional regression models. It is ...

Ink authentication is often complicated by tampering, aging, and chemical variability. Now, forensic scientists are turning ...

We trained models using logistic regression (LR) and four commonly used ML algorithms to predict NCGC from age-/sex-matched controls in two EHR systems: Stanford University and the University of ...

The penalized logistic regression model had an area under the curve of 0.854 and random forest models had a similar score of 0.848, which researchers said indicated these tools also did well.