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gradient-boosting
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A game theoretic approach to explain the output of any machine learning model.
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
Fit interpretable models. Explain blackbox machine learning.
A collection of research papers on decision, classification and regression trees with implementations.
Natural Gradient Boosting for Probabilistic Prediction
A curated list of data mining papers about fraud detection.
[UNMAINTAINED] Automated machine learning for analytics & production
A unified ensemble framework for PyTorch to improve the performance and robustness of your deep learning model.
A curated list of gradient boosting research papers with implementations.
LAMA - automatic model creation framework
python实现GBDT的回归、二分类以及多分类，将算法流程详情进行展示解读并可视化，庖丁解牛地理解GBDT。Gradient Boosting Decision Trees regression, dichotomy and multi-classification are realized based on python, and the details of algorithm flow are displayed, interpreted and visualized to help readers better understand Gradient Boosting Decision Trees
A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.
Building and training Speech Emotion Recognizer that predicts human emotions using Python, Sci-kit learn and Keras
A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.
Tuning hyperparams fast with Hyperband
A self-generalizing gradient boosting machine that doesn't need hyperparameter optimization
A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4.5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting, Random Forest and Adaboost w/categorical features support for Python