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tokens: We then investigated the possibility that these objects could form a cluster in some bi-dimensional projections of the parameter space. We tried all the possible magnitudes, colours, and redshifts combinations without finding any obvious clustering (some of these combinations are shown in Appendix C ). We also checked whether the group could correlate with a specific (high) error measure associated with any of the used features, but no any evident correlations were found. The nature of the objects in the overdensity region is still under further investigation. Figure 6. The scatter plot in the top left corner (a) shows the distribution of outliers in the SFRs spectroscopic versus SFRs photometric space with a superimposed density map, while the diagrams int the top right (b) and bottom left (c) corners show highlighted in orange all the objects with a density, respectively, six and eight times higher than the average point density. The histogram in the bottom right corner (d) shows the outliers density distribution. 4.7 Comparison with a recent work In order to compare our regression models with the k-NN used by Stensbo-Smidt et al. ( 2017 ) and their feature selection, we performed an experiment using the full training set and the set of 8 features found by Stensbo-Smidt et al. ( 2017 ). In Table 12 , we present the statistical results, which show a comparable performance among the three methods, although with a lower RMSE obtained by RF and MLPQNA. Using the features found by ΦLAB, the RF and MLPQNA can achieve even better performance, as shown in Table 6 . This is not surprising as k-NN is much more sensitive to the dimensionality of the parameter space (the so-called curse of dimensionality) than other two models. These latter can, therefore, take advantage of the information carried by a larger number of features than a k-NN model. Table 12. Comparison between our RF and MLPQNA against (Stensbo-Smidt et al. 2017 ) k-NN using the full train set and the best eight features found by Stensbo–Smidt. 5 DISCUSSION AND CONCLUSIONS In this work, based on our preliminary analysis of the problem presented at the ESANN-2018 conference (Delli Veneri et al. 2018 ), we estimated star formation rates for a large subset of the SDSS-DR7 and produced a catalogue of SFRs derived using photometric features only (magnitudes and colours) and the MLPQNA ML model (see Appendix B ) trained on a knowledge base of spectroscopically determined SFRs. By looking at Fig. 3 and the statistics in Table 6 , the regression results appear very promising. This is particularly true, considering that the dynamical range of SFR is between −12 and −7, and also that we have ∼5000 outliers out of the objects of the blind test set and, finally, taking into account the low percentage of outliers (∼2 per cent). However, from the results obtained by varying the size of the model training set (Tables 7 and 8 ), we think that a larger knowledge base of SFRs would further improve the