TensorFlow‘s Gradient Boosted Trees Model for structured data classification
Use TF’s Gradient Boosted Trees model in binary classification of structured data
- Build a decision forests model by specifying the input feature usage.
- Implement a custom Binary Target encoder as a Keras Preprocessing layer to encode the categorical features with respect to their target value co-occurrences, and then use the encoded features to build a decision forests model.
The model is implemented using Tensorflow 7.0 or higher. The US Census Income Dataset containing approximately 300k instances with 41 numerical and categorical variables was used to train it. This is a binary classification problem to determine whether a person makes over 50k a year.
Author: Khalid Salama
Adapted implementation: Tannia Dubon
Find the colab notebook at https://github.com/tdubon/TF-GB-Forest/blob/c0cf4c7e3e29d819b996cfe4eecc1f2728115e52/TFDecisionTrees_Final.ipynb
数据统计
数据评估
本站Ai导航提供的keras-io/TF_Decision_Trees都来源于网络，不保证外部链接的准确性和完整性，同时，对于该外部链接的指向，不由Ai导航实际控制，在2023年5月15日 下午3:20收录时，该网页上的内容，都属于合规合法，后期网页的内容如出现违规，可以直接联系网站管理员进行删除，Ai导航不承担任何责任。
相关导航
