1 citations · 1 across the 4 of their papers we have counts for
4 papers
EDGE: a closed-form directed test for the calibration of probabilistic binary classifiers
Ebrahim Khaled Ebrahim, Ahmed El-Kotory
A probabilistic binary classifier is judged almost everywhere by discrimination - accuracy, the ROC curve, the area under it. Every such criterion is invariant to a monotone distor…
Goodness-of-Fit Tests and Calibration Machine-Learning Algorithms for Logistic Regression with Sparse Data
Ebrahim Khaled Ebrahim
Assessing the goodness-of-fit of a logistic regression model is a critical prerequisite before the model is used for inference. However, goodness-of-fit (GOF) tests such as the chi…
Benchmarking Goodness-of-Fit and Calibration Algorithms for Logistic Regression Classifiers: A Large-Scale Simulation Study under Sparse Data
Ebrahim Khaled Ebrahim, Ahmed El-Kotory
Binary logistic regression is among the most widely used classification algorithms, yet a classifier is only trustworthy if its predicted probabilities are well calibrated. The cla…
A directional Hosmer-Lemeshow goodness-of-fit test for sparse logistic regression
Ebrahim Khaled Ebrahim, Ahmed El-Kotory
Goodness-of-fit assessment for the binary logistic regression model is difficult when covariates are continuous: the data are effectively sparse, the classical Pearson and deviance…