decision-trees

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 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.

Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG

🍊 :bar_chart: :bulb: Orange: Interactive data analysis

Python code for common Machine Learning Algorithms

A python library for decision tree visualization and model interpretation.

Text Classification Algorithms: A Survey

General Assembly's 2015 Data Science course in Washington, DC

A curated list of Best Artificial Intelligence Resources

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

Ollama for classical ML models. AOT compiler that turns XGBoost, LightGBM, scikit-learn, CatBoost & ONNX models into native C99 inference code. One command to load, one command to serve. 336x faster than Python inference.

Impress your boss with interactive Decision Tree visualization

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