- Statistics for Machine Learning
- Pratap Dangeti
- 101字
- 2021-07-02 19:05:56
When to stop tuning machine learning models
When to stop tuning the hyperparameters in a machine learning model is a million-dollar question. This problem can be mostly solved by keeping tabs on training and testing errors. While increasing the complexity of a model, the following stages occur:
- Stage 1: Underfitting stage - high train and high test errors (or low train and low test accuracy)
- Stage 2: Good fit stage (ideal scenario) - low train and low test errors (or high train and high test accuracy)
- Stage 3: Overfitting stage - low train and high test errors (or high train and low test accuracy)

推薦閱讀
- Java應用與實戰
- 精通JavaScript+jQuery:100%動態網頁設計密碼
- Building a Game with Unity and Blender
- Clojure for Domain:specific Languages
- Hands-On Enterprise Automation with Python.
- .NET 3.5編程
- Practical Game Design with Unity and Playmaker
- Spring技術內幕:深入解析Spring架構與設計原理(第2版)
- Hands-On Kubernetes on Windows
- Mastering HTML5 Forms
- Application Development with Swift
- 計算機應用基礎(Windows 7+Office 2010)
- Node.js Web Development
- 微信公眾平臺服務號開發:揭秘九大高級接口
- 新手學Visual C