What question did this study set out to answer?

The objective is to systematically analyze various Gradient Boosting models to evaluate their predictive performance and stability across multiple datasets.

June 15, 2026Machine Learning ResearchOpen Access

Gradient Boosting Revisited: Comparative Analysis of Selected Advances on Real-World Tabular Data

View Full Paper

Ask AI

Bookmark

Authors

MAMoses Apambila AgebureNavrongo Health Research Centre JWJapheth WireduRegent University College of Science and Technology SAStephen AkobreNavrongo Health Research Centre

Discussion

Member takes

Implication

Compares seven Gradient Boosting models in terms of accuracy and stability across datasets, suggesting their applicability varies with data characteristics.

Key Points

The objective is to systematically analyze various Gradient Boosting models to evaluate their predictive performance and stability across multiple datasets.
Conducted a systematic analysis of seven models: XGBoost, LightGBM, CatBoost, HistGradientBoosting, GradientBoosting, AdaBoost, and MorphBoost.
Trained models on ten benchmark datasets with a fixed 80:20 train-test split and 3-fold cross-validation.
Measured performance using accuracy, F1-score, and ROC-AUC metrics.
CatBoost achieved the highest mean accuracy of 0.9400 and a near-perfect ROC-AUC of 0.9915.
HistGradientBoosting was identified as the most stable model, followed by LightGBM and XGBoost.
MorphBoost shows promise in binary and high-dimensional datasets but lacks support for multiclass handling.

Cite This Study

Agebure et al. (2026) studied this question.

synapsesocial.com/papers/6a2f980ca1cfeec4908290f7 https://doi.org/10.11648/j.mlr.20261101.14

View Full Paper

Ask AI

Bookmark

June 15, 2026Machine Learning ResearchOpen Access

Gradient Boosting Revisited: Comparative Analysis of Selected Advances on Real-World Tabular Data

View Full Paper

Ask AI

Bookmark

Authors

MAMoses Apambila AgebureNavrongo Health Research Centre JWJapheth WireduRegent University College of Science and Technology SAStephen AkobreNavrongo Health Research Centre

Discussion

Member takes

Implication

Compares seven Gradient Boosting models in terms of accuracy and stability across datasets, suggesting their applicability varies with data characteristics.

Key Points

The objective is to systematically analyze various Gradient Boosting models to evaluate their predictive performance and stability across multiple datasets.
Conducted a systematic analysis of seven models: XGBoost, LightGBM, CatBoost, HistGradientBoosting, GradientBoosting, AdaBoost, and MorphBoost.
Trained models on ten benchmark datasets with a fixed 80:20 train-test split and 3-fold cross-validation.
Measured performance using accuracy, F1-score, and ROC-AUC metrics.
CatBoost achieved the highest mean accuracy of 0.9400 and a near-perfect ROC-AUC of 0.9915.
HistGradientBoosting was identified as the most stable model, followed by LightGBM and XGBoost.
MorphBoost shows promise in binary and high-dimensional datasets but lacks support for multiclass handling.

Cite This Study

Agebure et al. (2026) studied this question.

synapsesocial.com/papers/6a2f980ca1cfeec4908290f7 https://doi.org/10.11648/j.mlr.20261101.14

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

View Full Paper

Ask AI

Bookmark