Building Decision Trees From Scratch In Python
AiLearning：数据分析+机器学习实战+线性代数+PyTorch+NLTK+TF2
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.
Machine learning, in numpy
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.
A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
Text Classification Algorithms: A Survey
