The lack of a generalizable machine learning model for predicting the safety of food for 1
human consumption is a significant challenge for policymakers and responsible authorities. This 2
study provides a step-by-step guide to predict the results of seafood product import inspections, 3
focusing on identifying and understanding the critical factors that influence these results. By compar- 4
ing the performances of an ensemble of machine learning models, this study combines the strengths 5
of multiple algorithms to improve the predictive accuracy and gain insights into the key factors 6
impacting them. The ensemble model based on the soft voting technique achieves superior perfor- 7
mance to that based on the hard voting technique in terms of the recall and area under the curve 8
(AUC) scores. The study discovered that various characteristics, such as the exporting country ratio, 9
major product category, overseas manufacturer ratio, importer ratio, and seasonal variation, had a 10
substantial influence on the models’ decisions. This research guide for predicting seafood product 11
import inspection results could pave the path for other items to follow.