Department of Food Science and Engineering, Shandong Agricultural University, Tai’an 271018, Shandong, China
Mini Review
Improved Random Forest-Based Risk Prediction Model for Food Safety with Virtual Sample Integration
Author(s): Qinzhi Wang*
Food safety is an important concern for consumers and food producers alike. With the increasing complexity of the food supply chain and the global
nature of the food industry, the risk of foodborne illness has become a major public health issue. Risk prediction models are an important tool for
food safety management, as they can help identify high-risk products, processes, and supply chains. In this article, we will discuss an improved
random forest-based risk prediction model for food safety with virtual sample integration. Random forest is a popular machine learning algorithm
that is widely used for risk prediction in various fields, including healthcare, finance, and ecology. Random forest is an ensemble learning method
that combines multiple decision trees to generate a robust and accurate prediction model. In food safety, random forest has been used to predict.. Read More»
DOI:
10.37421/2736-6189.2023.8.324
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