Examples of the F1-scores from individual base-learners and the model ensembling methods. Left: Selection of quiescent galaxies at z = 2–2.5 from the Int Wide catalogue using Euclid photometry, without foreknowledge of galaxy redshifts. As described in the text, the meta-learner performs a non-linear fusion of the individual classifiers, resulting in a significantly higher Fl-score than obtained by any of the individual base learners or the two other ensemble methods (averaging and hard-voting). Centre: Impact of ensembling a LightGBMClassifier model, the hyperparameters of which are well-tuned for this problem (LightGBM 1), with four other LightGBM models that have poorly tuned hyperparameters (LightGBM 2,3,4,5). In this case, averaging the model predictions and hard-voting both produce poor results, but the meta-learner is able to identify and weight accordingly the low quality class predictions. Right: Application of a meta-learner to a classification model produced by a single base-learner, in this case XGBoostClassifier. In this circumstance, the meta-learner performs 'error correction', resulting in a significant improvement in the quality of the classifier.