课程详情
Ensembles involve groups of models working together to make more accurate predictions. When creating complete deployed solutions, data scientists may also leverage passing data from one model to another or using models in combination—also known as metamodeling. These techniques are dominant among winners of modeling competitions like Kaggle as well as leading data science teams around the world. In this advanced course, you can learn how to add ensembles and metamodeling to your toolset. Instructor Keith McCormick provides a conceptual introduction that can be applied in any program: R, Python, SPSS, or SAS. He introduces the most essential ensemble algorithms and explains the basics of metamodeling. Plus, review two case studies that show how to combine supervised and unsupervised ensembles and how to route subpopulations of data to different models in a metamodeling scenario.
您将获得的技能
了解讲师
学员评价
-
-
Bilal Khan, CFA
Bilal Khan, CFA
Risk & Analytics at CURO | Strategy | Advisory | Digital Transformation | Fintech | Data Science | Machine Learning
-
Dr. Christian Schenk
Dr. Christian Schenk
Entwicklungsingenieur & Respekt-Trainer | Ich helfe Dir dabei, durch respektvolle Kommunikation resilienter zu werden | Respekt | Resilienz |…
内容
课程内容
- 边学边练 1 个练习文件
- 随时随地学习 可在平板电脑和手机上访问
相似课程
下载课程
使用 iOS 或安卓版领英学习 APP，即可在移动设备上离线观看课程。