基于CatBoost算法的收入预测开题报告

1. 研究目的与意义（文献综述）

2. 研究的基本内容与方案

2.1基本内容

（1）学习并掌握python程序设计；

3. 研究计划与安排

(1)2020/1/13—2020/2/28：确定选题，查阅文献，外文翻译和撰写开题报告；

(2)2020/3/1—2020/4/30：catboost算法设计与实现、模型训练、模型测试与实验分析；

4. 参考文献（12篇以上）

[1]prokhorenkova, liudmila, gleb gusev,aleksandr vorobev, anna veronika dorogush, and andrey gulin. "catboost:unbiased boosting with categorical features." in advances in neuralinformation processing systems, pp. 6638-6648. 2018.

[2]chen, tianqi, and carlos guestrin."xgboost: a scalable tree boosting system." in proceedings of the22nd acm sigkdd international conference on knowledge discovery and datamining, pp. 785-794. 2016.

[3]ke, guolin, qi meng, thomas finley,taifeng wang, wei chen, weidong ma, qiwei ye, and tie-yan liu. "lightgbm:a highly efficient gradient boosting decision tree." in advances in neuralinformation processing systems, pp. 3146-3154. 2017.