基于机器学习的焦油预测模型研究

Study on predicting models for tar yields of cigarettes based on machine learning

为研究卷烟焦油预测模型,以焦油的释放量为研究对象,运用不同的回归方法进行焦油预测研究,以各个模型的标准化均方误差为评判尺度,对各个模型的预测效果进行了比较。结果表明,各模型的预测精度差别较大,整体来看机器学习方法对于焦油的预测精度较高,其中以随机森林算法回归对于焦油的预测精度最高,表现出较高的预测精度和良好的稳定性,其次表现较好的机器学习算法为支持向量机回归方法。因此,在焦油预测应用或研究中可以运用随机森林或其他机器学习方法对焦油进行建模预测。

To improve the accuracy of predicting tar yield in cigarettes, several machine learning methods and the ordinary liner regression were used to predict tar yield. The standardized mean square error was set as the criterion to judge the model＇s predicting accuracy. The results indicated that significant differences among indi- vidual regression models were observed. The machine learning methods showed a higher accuracy of predicting tar yield than that of the traditional simple liner regression. Random forest regression performed the best for pre- dicting tar yield in these models and its performance was stable and precise. The second best model should be the support vector machine regression. Thus, machine yield and other tobacco research areas. learning methods could be widely applied in predicting tar