Blue-Yonder-OSS/cyclic-boosting
implementation of Cyclic Boosting machine learning algorithms
This algorithm helps data scientists and machine learning engineers build predictive models that are easier to understand. You input structured data (features and target outcomes) and it outputs predictions along with clear insights into how each feature contributes to those predictions, making complex models more transparent for business stakeholders. This is for data scientists or ML engineers.
No commits in the last 6 months.
Use this if you need to develop machine learning models where not just the prediction, but also the 'why' behind it, is crucial for business interpretation or regulatory compliance.
Not ideal if your primary concern is achieving the absolute highest predictive accuracy without any need for model interpretability or if you are not comfortable working with machine learning libraries.
Stars
95
Forks
15
Language
Python
License
EPL-2.0
Category
Last pushed
Sep 02, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/Blue-Yonder-OSS/cyclic-boosting"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
dmlc/xgboost
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python,...
catboost/catboost
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for...
stanfordmlgroup/ngboost
Natural Gradient Boosting for Probabilistic Prediction
lightgbm-org/LightGBM
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework...
fabsig/GPBoost
Tree-Boosting, Gaussian Processes, and Mixed-Effects Models