## 3 Results
### Feature Selection  
The goal of feature selection is to rank the features according to their importance. First, we used five selection algorithms for this purpose and ensembled their rankings using Reciprocal Rank as stated in Section 2.4. The ranking of the latter is usually superior of the individual ones, and is the one that was used in this work. The reason of its success is firstly that it takes as inputs rankings of diverse feature selection techniques, where the disadvantages of one might be compensated by another, and secondly that it is a harmonic mean formula. This means that if one of the five algorithms ranks a feature among the top and the other four rank it lower, then it will remain in a high position on the final ensemble ranking since the harmonic mean is biased towards the smaller of the numbers. Figure 3 gives a graphical insight of the exceptional strength of feature selection and the boost that it offers to the regression problem. It is a backward stepwise selection process, starting with all the features and eliminating them according to their rank of diminishing importance. All runs used 10-fold cross validation for training and evaluation of the model using XGBoost. A clear pattern to each species seems present. Using the baseline of all features (leftmost side of plots), the Root Mean Square \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & **XGBoost** & **GBM** & **LGBM** & **ET** & **RF** & **AdaBoost** & **DT** & **LARS** & **SVR** & **LR** \\ \hline _Engranuis carcassions_ & 0.2369 & 0.2403 & 0.2359 & 0.2439 & 0.2495 & 0.2432 & 0.2784 & 0.3527 & 0.3491 & 0.3724 \\ _Sartina pikchardus_ & 0.1807 & 0.1820 & 0.1889 & 0.1872 & 0.1978 & 0.1916 & 0.2621 & 0.3510 & 0.3488 & 0.3670 \\ _Sartinaella auratia_ & 0.2548 & 0.2557 & 0.2484 & 0.2506 & 0.2482 & 0.2587 & 0.3283 & 0.3460 & 0.3432 & 0.3528 \\ _Sourne colitis_ & 0.1783 & 0.1758 & 0.1795 & 0.1895 & 0.1772 & 0.1924 & 0.2377 & 0.2448 & 0.2422 & 0.2662 \\ _Sourne scomlrus_ & 0.1254 & 0.1276 & 0.1365 & 0.1317 & 0.1376 & 0.1351 & 0.1694 & 0.2158 & 0.2164 & 0.2191 \\ _Spirana snaris_ & 0.1221 & 0.1283 & 0.1267 & 0.1258 & 0.1201 & 0.1427 & 0.1670 & 0.1378 & 0.1649 & 0.1816 \\ _Tharms thymus_ & 0.0862 & 0.0868 & 0.0875 & 0.0916 & 0.0913 & 0.0969 & 0.1160 & 0.2322 & 0.2305 & 0.2397 \\ _Xiphias gladins_ & 0.0794 & 0.0781 & 0.0766 & 0.0805 & 0.0823 & 0.0879 & 0.1127 & 0.2228 & 0.2209 & 0.2227 \\ \hline Mean Score & 0.1579 & 0.1593 & 0.1600 & 0.1626 & 0.1630 & 0.1685 & 0.2089 & 0.2628 & 0.2645 & 0.2776 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Algorithm comparison using RMSE of the 20% test set of locations using all the features as predictors for baseline selection per species. Error is relatively high and stays there up to around 70% of features removed (0.3 feature cutoff). From that point it decreases, until it reaches its lowest value at a percentage of around 2% features retained (i.e., around 136 features). The 2% percentage slightly varies between species (from 1.5% to 2.5%), but as a general model is constructed, the, 2% cutoff will be used. If even less features are used, then performance drops (RMSE increases again, drastically). This behavior is similar for all species, regardless of the actual RMSE values, which vary depending on the species. A reason for this outcome is that all datasets were created in a similar manner. The feature engineering procedure created the same features for each species. This does not mean that the top 2% of them are the same. Each species, varies on the predictors that are affecting its distribution. The baseline runs for all species using the XGBoost algorithm are improved, as it can be seen in Table 4, from 26.3% for aurita to even 45.5% for thymusn. The average performance boost for the eight species is about 34% on the test set. As it can be seen with this evaluation, the results on the validation and the test set are similar, indicating lack of overfitting. Figure 3: Root Mean Square Error (RMSE) of the predictions for the eight species as the number of features (%) decreases.