Scikit-learn was used to fit logistic regression models, and a train/test split was created on the data, with test data only used for evaluating the performance of the models.

Logistic regression is a popular method for detecting uniform and nonuniform differential item functioning (DIF) effects. Theoretical formulas for the power and sample size calculations are derived ...

If a logistic regression model is trained for too many epochs, the model will overfit, meaning the model will predict very well for the training data, but predict poorly for the test data.

The simplest form of regression in Python is, well, simple linear regression. With simple linear regression, you're trying to ...

Methods 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 ...

Results Logistic regression models achieved a test area under the receiver operating curve of 0.73, F -score of 0.79, accuracy of 0.71, and Brier score of 0.29, demonstrating good calibration, ...

For up to four categories and given sufficient replications per design point (eight or more seem to be enough), an adjusted likelihood ratio statistic for testing the goodness of fit of multinomial ...