20.500.12556/DiRROS-17082 MsGEN measuring generalization of nutrient value prediction across different recipe datasets MsGEN: measuring generalization of nutrient value prediction across different recipe datasets In this study, we estimate the generalization of the performance of previously proposed predictive models for nutrient value prediction across different recipe datasets. For this purpose, we introduce a quantitative indicator that determines the level of generalization of using the developed predictive model for new unseen data not presented in the training process. On a predefined corpus of recipe embeddings from six publicly available recipe datasets (i.e., projecting them in the same meta-feature vector space), we train predictive models on one of the six recipe datasets and test the models on the rest of the datasets. In parallel, we define and calculate generalizability indexes which are numbers that indicate how generalizable a predictive model is i.e., how well will a predictive model learned on one dataset perform on another one not involved in the training. The evaluation results prove the validity of these indexes – their relation with the accuracy of the predictions. Further, we define three sampling techniques for selecting representative data instances that will cover all parts from the feature space uniformly (involving data from all datasets) and further will improve the generalization of a predictive model. We train predictive models with these generalized datasets and test them on instances from the six recipe datasets that are not selected and included in the generalized datasets. The results from the evaluation of these predictive models show improvement compared to the results from the predictive models trained on one recipe dataset and tested on the others separately. ML pipeline predictive modeling nutrient prediction recipe datasets strojno učenje true false true Elsevier Nizozemska Angleški jezik Slovenski jezik © 2023 The Authors. Published by Elsevier Ltd. Neznano 2023-09-25 14:26:49 2023-09-25 14:26:51 2023-09-26 03:36:55 0000-00-00 00:00:00 2023 0 0 Nasl. z nasl. zaslona; Soavtorji: Tome Eftimov, Sašo Džeroski, Barbara Koroušić Seljak; Opis vira z dne 20. 9. 2023; str. 1-40 Vol. , article no. 121507 2023 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2023-08-18 2023-09-06 2023-09-16 004.8 1873-6793 10.1016/j.eswa.2023.121507 165116419 https://www.sciencedirect.com/science/article/pii/S0957417423020092?via%3Dihub 1 fdb6c38d-5b8d-11ee-8cc8-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=23192 RAZ_Ispirova_Gordana_2023.pdf RAZ_Ispirova_Gordana_2023.pdf 1 590CD5C276E2F45AADC6BCF60EF204BB 11fe2905a3541f870ad00dc9385b624a4e5d8cbc056da9531167217c3560662c 426e4e12-5b90-11ee-8cc8-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=23194 Institut Jožef Stefan 0 0 0