wepe/tgboost
Tiny Gradient Boosting Tree
This tool helps data scientists and machine learning engineers build predictive models from structured data. It takes your raw datasets (like CSV files) containing features and target variables, and produces a trained model that can make predictions (e.g., classifications or regressions) on new, unseen data. You can also analyze which features were most important in the model's decisions.
323 stars. No commits in the last 6 months.
Use this if you need a high-performance, gradient boosting tree model for classification or regression tasks, especially when dealing with datasets that have missing values or categorical features.
Not ideal if your primary need is for deep learning models, real-time streaming data processing, or models that require GPU acceleration.
Stars
323
Forks
103
Language
Java
License
MIT
Category
Last pushed
Jun 13, 2019
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/wepe/tgboost"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
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