Aguilar-Argüello G.Fuentes-Pineda G.Hernández-Toledo H.M.Martínez-Vázquez L.A.Vázquez-Mata J.A.Brough S.Demarco R.Ghosh A.Jiménez-Teja Y.Martin G.Pearson W.J.Sifón C.2025-03-072025-03-072025-02Monthly Notices of the Royal Astronomical Society Volume 537, Issue 2, Pages 876 - 8961 February 20250035-8711https://repositorio.unab.cl/handle/ria/63676Indexación: ScopusWe employ the eXtreme Gradient Boosting (XGBoost) machine learning (ML) method for the morphological classification of galaxies into two (early-type, late-type) and five (E, S0–S0a, Sa–Sb, Sbc–Scd, Sd–Irr) classes, using a combination of non-parametric (C, A, S, AS, Gini, M20, c5090), parametric (Sérsic index, n), geometric (axial ratio, BA), global colour (g − i, u − r, u − i), colour gradient [∆(g − i)], and asymmetry gradient (∆A9050) information, all estimated for a local galaxy sample (z < 0.15) compiled from the Sloan Digital Sky Survey imaging data. We train the XGBoost model and evaluate its performance through multiple standard metrics. Our findings reveal better performance when utilizing all 14 parameters, achieving accuracies of 88 per cent and 65 per cent for the two-class and five-class classification tasks, respectively. In addition, we investigate a hierarchical classification approach for the five-class scenario, combining three XGBoost classifiers. We observe comparable performance to the ‘direct’ five-class classification, with discrepancies of only up to 3 per cent. Using Shapley Additive Explanations (an advanced interpretation tool), we analyse how galaxy parameters impact the model’s classifications, providing valuable insights into the influence of these features on classification outcomes. Finally, we compare our results with previous studies and find them consistently aligned.engalaxies: generalgalaxies: structuremethods: data analysismethods: miscellaneousArtículoAttribution 4.0 International CC BY 4.0 Deed10.1093/mnras/staf085