基于Adaboost的随机森林算法在医疗销售预测中的应用

摘要

提出一种基于Adaboost方法的随机森林销售量预测方法. 首先对销售量的影响因素进行了特征分析, 确定了训练数据的特征和维度. 然后采用基于Adaboost的随机森林销量预测方法对特征数据进行训练并给出了预测算法的步骤. 最后使用python进行了仿真实验, 实验结果表明, 该方法可以有效提高随机森林的回归性能, 且预测精度高, 具有较强的泛化能力.%A sales forecasting method based on random forest algorithm and Adaboost method is proposed. Firstly, by analyzing the characteristics of the sales factors, the characteristics and dimensions of the training data are determined. Then, the feature data is trained by the random forest algorithm based on Adaboost, and the steps of the prediction algorithm are presented. Finally, the experimental results show that this method can greatly enhance the performance of random forest algorithm, and has a high prediction accuracy, as well as a good performance of generalization.