Machine learning, in numpy

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

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

Tuning hyperparams fast with Hyperband

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

Compiler for LightGBM gradient-boosted trees, based on LLVM. Speeds up prediction by ≥10x.

An experimental Python package that reimplements AutoGBT using LightGBM and Optuna.

Competing Risks and Survival Analysis

Supporting code for the paper "Finding Influential Training Samples for Gradient Boosted Decision Trees"

The official implementation for ECCV22 paper: "FOSTER: Feature Boosting and Compression for Class-Incremental Learning" in PyTorch.

An implementation of "Multi-Level Network Embedding with Boosted Low-Rank Matrix Approximation" (ASONAM 2019).