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.

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.

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 ...

We show that simulation can be used to obtain the critical values for such tests in the low dimensional setting and demonstrate using both theoretical results and extensive numerical studies that some ...

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, ...