xgboost

19 projets partagent ce topic GitHub

Les traits relient les membres réellement apparentés entre eux. La taille des points suit les étoiles.

🧬 Membres

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java,…

Python for《Deep Learning》，该书为《深度学习》(花书)…

Standardized Distributed Generative and Predictive AI Inference Platform for Scalable, Multi-Framework…

Alink is the Machine Learning algorithm platform based on Flink, developed by the PAI team of Alibaba…

mljar-supervised

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and…

A python library for decision tree visualization and model interpretation.

Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell,…

A library for debugging/inspecting machine learning classifiers and explaining their predictions

Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas,…

Deep Learning Server and CLI for Torch and TensorRT

awesome-decision-tree-papers

A collection of research papers on decision, classification and regression trees with implementations.

Distributed AI Model Training and LLM Fine-Tuning on Kubernetes

Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri.…

A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R…

Provide an input CSV and a target field to predict, generate a model + code to run it.

[UNMAINTAINED] Automated machine learning for analytics & production

MLBox is a powerful Automated Machine Learning python library.

Python library for time series forecasting using scikit-learn compatible models, statistical methods, and…

The missing bridge between your ML models and your AI agents.

🔗 Familles voisines

lightgbm 10 scikit-learn 64 automl 39 automated-machine-learning 13 random-forest 12 feature-engineering 23 catboost 4 keras 105

Mesuré à partir des topics GitHub communs aux deux projets, pondérés par leur rareté.