- Deep Learning Quick Reference
- Mike Bernico
- 152字
- 2021-06-24 18:40:09
Defining our cost function
For regression tasks, the most common cost functions are Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). I'll be using MAE here. It is defined as follows:

Very simply, MAE is the average unsigned error for all examples in the dataset. It's very similar to RMSE; however, we use the absolute value of the difference between y and instead of the square root of the average squared error:

You might be wondering how MAE differs from the more familiar RMSE. In cases where the error is evenly distributed across the dataset, RMSE and MAE will be equal. In cases where there are very large outliers in a dataset, RMSE will be much larger than MAE. Your choice of cost function should be appropriate to your use case. In regard to interpretability, MAE is more interpretable than RMSE as it's the actual average error.
推薦閱讀
- Microsoft Power BI Quick Start Guide
- Practical Ansible 2
- ETL with Azure Cookbook
- Python Artificial Intelligence Projects for Beginners
- 手把手教你玩轉RPA:基于UiPath和Blue Prism
- DevOps:Continuous Delivery,Integration,and Deployment with DevOps
- 網絡化分布式系統預測控制
- 工業機器人實操進階手冊
- PowerPoint 2003中文演示文稿5日通
- 人工智能:重塑個人、商業與社會
- 百度智能小程序:AI賦能新機遇
- 服務科學概論
- 小數據之美:精準捕捉未來的商業小趨勢
- Learning VMware App Volumes
- 自動控制原理