Author

Ebrahim Khaled Ebrahim

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Preprint Aug 2026

EDGE: a closed-form directed test for the calibration of probabilistic binary classifiers

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 distortion of the predicted probabilities, so a classifier can rank perfectly and still return probabilities that are badly wrong. Calibration is...

Ebrahim Khaled Ebrahim, Ahmed El-Kotory · 1 citation

Preprint Aug 2026

Goodness-of-Fit Tests and Calibration Machine-Learning Algorithms for Logistic Regression with Sparse Data

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-square and deviance tests often give invalid results when the data are"sparse"-- a common issue with continuous predictors like age or weig...

Ebrahim Khaled Ebrahim · 0 citations

Preprint Jul 2026

A directional Hosmer-Lemeshow goodness-of-fit test for sparse logistic regression

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 tests fail, and practitioners rely on partition-based tests, such as the Hosmer-Lemeshow test, that group observations before comparing obs...

Ebrahim Khaled Ebrahim, Ahmed El-Kotory · 1 citation

Preprint Jul 2026

Benchmarking Goodness-of-Fit and Calibration Algorithms for Logistic Regression Classifiers: A Large-Scale Simulation Study under Sparse Data

This paper provides a unified taxonomy and a large-scale, reproducible simulation benchmark; more than twenty tests are implemented in the open-source R package ebrahim.gof, and translate these findings into practical, evidence-based guidance for assessing logistic regression fit.

Ebrahim Khaled Ebrahim, Ahmed El-Kotory · 0 citations