基于优化随机森林算法预测食品检验不合格指标

目的 建立基于优化的随机森林算法模型实现对食品不合格指标的分类预测.方法 通过收集山东省食品药品监督管理局2015—2019年食品安全抽样检验产生的不合格数据,并对其进行多项数据预处理操作,采用超参数网格搜索和10折交叉验证方法建立基于随机森林的食品不合格指标的分类预测模型,并通过对传统随机森林模型的参数优化,将其与决...

Prediction of unqualified index of food inspection based on optimized random forest algorithm

Objective Food unqualified indicators endanger human dietary health. This paper applies data mining technology to food safety testing.Methods Through the collection of unqualified data generated by the food safety sampling inspection from 2015 to 2019 issued by the official website of Shandong Food and Drug Administration, and a number of data preprocessing operations, the hyperparameter grid search and 10-folds cross-validation method are used to establish A classification prediction model based on random forest-based food unqualified indicators. In addition, by optimizing the parameters of the traditional random forest model, it is classified with decision tree (DT), logistic regression (LR) and gradient boosting decision tree (GBDT) algorithms The forecast results were compared.Results Experiments show that the random forest model after parameter optimization can achieve 89.4% prediction accuracy of unqualified indicators in food, which is 11% higher than the DT algorithm, 9% higher than the LR algorithm, and 8.1% higher than the GBDT algorithm.Conclusion The optimized random forest model can complete the classification and prediction task of food unqualified indicators, and has broad application prospects.