## 1 Introduction
Several researchers have developed models for various species using different training samples, predictors, study areas, machine learning algorithms, and models [17; 18; 19; 20; 21; 22; 23; 24; 25]. Almost all models follow the presence/absence approach with observation records obtained from online data collections like OBIS, GBIF, Catalog of Life, ICES, Reef Life Survey (RLS) [17; 19; 20; 22], museums and literature [20; 23; 25], environmental projects [22], and own sampling [21; 26]. Some studies also create pseudo-absence records in their attempt to device a reliable model [17; 27]. A potential drawback of the above mentioned data-driven models would be the generally limited records in the databases used (ranging from 30 to 8000, with 250 on average). The spatial resolution of the variables varies depending on spatial coverage--mostly 0.5 arc-minutes for large-scale studies, with the exceptions of low resolutions of 1 to 2 arc-minutes [23] and high resolutions of 0.05 arc-minutes for local studies [28]. The most frequently studied seas are the Atlantic [20; 23] and the North Sea [22], while there are a few models for the Mediterranean, mostly for regional seas [18; 28], and rarely for the whole basin [17]. The machine learning algorithms that have been used in these fishery SDMs are Logistic Regression [17], other Generalized Linear Models (GLM) [19; 22; 25], Support Vector Machines (SVM) [17; 22], Gradient Boosting Models (GBM) [22; 25], Decision Trees [18], Genetic Algorithms for Rule Set Production (GARP) [22; 23], Random Forests [19; 22; 25], Multivariate Adaptive Regression Splines (MARS) [22; 25], Maximum Entropy (MaxEnt) [19; 22; 25; 29] and Artificial Neural Network (ANN) [17; 18]. Some studies use ensembles [17], while others test existing environmental models [17; 19; 22] and Favourability Functions [30]. Regarding the number of predictors, to our knowledge, all studies except one [29] used about 5 to 20 features. Very few took advantage of feature selection and variable importance pre-processing, and the ones that did used correlation [19; 28], some filter methods [18], permutation importance [25], and embedded MaxEnt variable importance [29]. Finally, there are various SDM approaches [24], that model the distribution of multiple species and their interrelations simultaneously. These approaches are out of scope of the current work. As it can be concluded from the previous works and according to [PERSON] et al. [20] and [PERSON] et al. [31], there are some limitations to SDMs and most of them can be improved in a number of ways. Typically, there are insufficient observations and a limited number of environmental variables available to train effective machine learning models, presence-absence data are unbalanced, absence data are often not available or artificially created, resolution is poor, and there is collinearity in the data. Therefore, the principal aim of the present work is to overcome these challenges. More specifically, the herein developed model was trained with roughly \(\times\)3.5 times more observations than the average related work (improvement over observations). With the use of feature engineering, 6830 features were extracted and subsequently, feature selection was performed, leading to the selection of the most important ones (improvement over features and collinearity). Finally, a machine learning algorithm that has never been applied in previous SDM literature, namely, XGBoost [32] was used. XGBoost offers regularization preventing the model from overfitting, it can handle missing values and it provides the optimum number of boosting iterations in a single run, minimizing the time needed for its performance. Instead of using presence-absence data for binary classification, like most studies do, the problem was transformed to a regression one (improving the unbalance of presence-absence datasets) and predict the probability of occurrence based on data provided by the AquaMaps database.