gradient-boosting

#计算机科学#A game theoretic approach to explain the output of any machine learning model.

#计算机科学#LightGBM是一个基于决策树算法的分布式梯度提升框架（GBT、GBDT、GBRT、GBM或MART），用于排名、分类和许多其他机器学习任务。

#计算机科学#Machine learning, in numpy

#计算机科学#A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

#计算机科学#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 computa...

#计算机科学#Fit interpretable models. Explain blackbox machine learning.

#计算机科学#A collection of research papers on decision, classification and regression trees with implementations.

#计算机科学#Natural Gradient Boosting for Probabilistic Prediction

#计算机科学#A curated list of data mining papers about fraud detection.

#计算机科学#[UNMAINTAINED] Automated machine learning for analytics & production

#计算机科学#A unified ensemble framework for PyTorch to improve the performance and robustness of your deep learning model.

#计算机科学#A curated list of gradient boosting research papers with implementations.

#自然语言处理#LAMA - automatic model creation framework

#计算机科学#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 a...

#计算机科学#A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.

#计算机科学#Building and training Speech Emotion Recognizer that predicts human emotions using Python, Sci-kit learn and Keras

#计算机科学#A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.

#计算机科学#Tuning hyperparams fast with Hyperband

#计算机科学#A self-generalizing gradient boosting machine that doesn't need hyperparameter optimization

#计算机科学#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 ...