We have recently open-sourced a pure-Python implementation of Cyclic Boosting, a family of general-purpose, supervised machine learning algorithms. Its predictions are fully explainable on individual sample level, and yet Cyclic Boosting can deliver highly accurate and robust models. For this, it requires little hyperparameter tuning and minimal data pre-processing (including support for missing information and categorical variables of high cardinality), making it an ideal off-the-shelf method for structured, heterogeneous data sets. Furthermore, it is computationally inexpensive and fast, allowing for rapid improvement iterations. The modeling process, especially the infamous but unavoidable feature engineering, is facilitated by automatic creation of an extensive set of visualizations for data dependencies and training results. In this presentation, we will provide an overview of the inner workings of Cyclic Boosting, along with a few sample use cases, and demonstrate the usage of the new Python library.
You can find Cyclic Boosting on GitHub: https://github.com/Blue-Yonder-OSS/cyclic-boosting
We have recently open-sourced a pure-Python implementation of Cyclic Boosting, a family of general-purpose, supervised machine learning algorithms. Its predictions are fully explainable on individual sample level, and yet Cyclic Boosting can deliver highly accurate and robust models. For this, it requires little hyperparameter tuning and minimal data pre-processing (including support for missing information and categorical variables of high cardinality), making it an ideal off-the-shelf method for structured, heterogeneous data sets. Furthermore, it is computationally inexpensive and fast, allowing for rapid improvement iterations. The modeling process, especially the infamous but unavoidable feature engineering, is facilitated by automatic creation of an extensive set of visualizations for data dependencies and training results. In this presentation, we will provide an overview of the inner workings of Cyclic Boosting, along with a few sample use cases, and demonstrate the usage of the new Python library.
You can find Cyclic Boosting on GitHub: https://github.com/Blue-Yonder-OSS/cyclic-boosting
