- Hands-On Natural Language Processing with Python
- Rajesh Arumugam Rajalingappaa Shanmugamani
- 143字
- 2021-08-13 16:01:47
One-hot encoding
One-hot encoding is a vectorization technique for labeled data, especially categorical data. In the case of binary labels, target variables will be presented as [0, 1], [1, 0]. The same representation for three classes will appear as [0, 0, 1], [0, 1, 0], [1, 0, 0]. This type of representation can support any number of categories. The main advantage of one-hot encoding is that it treats all categorical data equally, in contrast to arbitrary categorical labels. For instance, categories to represent colors such as red, green, and blue, may use integers such as 0, 1, and 2. Although there is no intrinsic order for colors, some ML models may treat such input as if it has an order. This is avoided in one-hot encoding, as it does not assume any order in the categorical values since they are binary encoded.
- ServiceNow Application Development
- Web前端開發技術:HTML、CSS、JavaScript(第3版)
- 數據庫程序員面試筆試真題與解析
- Beginning Java Data Structures and Algorithms
- Docker技術入門與實戰(第3版)
- Python網絡爬蟲從入門到實踐(第2版)
- Python面向對象編程:構建游戲和GUI
- Python爬蟲、數據分析與可視化:工具詳解與案例實戰
- 持續集成與持續交付實戰:用Jenkins、Travis CI和CircleCI構建和發布大規模高質量軟件
- Hands-On Neural Network Programming with C#
- 深度學習原理與PyTorch實戰(第2版)
- Troubleshooting Citrix XenApp?
- Oracle 12c從入門到精通(視頻教學超值版)
- 軟件工程與UML案例解析(第三版)
- Access數據庫應用教程(2010版)