awesome-repositories.com
Blog
awesome-repositories.com

Discover the best open-source repositories with AI-powered search.

ExploreCurated searchesOpen-source alternativesSelf-hosted softwareBlogSitemap
ProjectAboutHow we rankPressMCP server
LegalPrivacyTerms
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·
grf-labs avatar

grf-labs/grf

0
View on GitHub↗
1,092 stars·282 forks·C++·GPL-3.0·1 viewgrf-labs.github.io/grf↗

Grf

Generalized Random Forests

Features

  • Decision Tree Models - Generalized random forest implementation.

Star history

Star history chart for grf-labs/grfStar history chart for grf-labs/grf

AI search

Explore more awesome repositories

Describe what you need in plain English — the AI ranks thousands of curated open-source projects by relevance.

Start searching with AI

Open-source alternatives to Grf

Similar open-source projects, ranked by how many features they share with Grf.
  • cerlymarco/linear-treeC

    cerlymarco/linear-tree

    0View on GitHub↗
    View on GitHub↗0
  • dmlc/xgboostdmlc avatar

    dmlc/xgboost

    28,471View on GitHub↗

    XGBoost is a distributed machine learning library for implementing scalable gradient boosting decision trees used for regression, classification, and ranking. It functions as a predictive model framework and a cross-language toolkit, providing a core implementation with native bindings for Python, R, Java, Scala, and C++. The system is designed as a GPU-accelerated library that utilizes CUDA and NCCL to speed up the training of decision tree ensembles. It operates as a distributed framework capable of scaling training and prediction across multi-node clusters and GPU environments to process m

    C++distributed-systemsgbdtgbm
    View on GitHub↗28,471
  • h2oai/h2o-3h2oai avatar

    h2oai/h2o-3

    7,493View on GitHub↗

    h2o-3 is a distributed machine learning platform and automated machine learning framework designed for training and deploying predictive models using distributed in-memory computing. It functions as a deep learning framework and a distributed model scoring engine, capable of operating as a Kubernetes ML cluster to process large datasets in parallel. The platform distinguishes itself through automated machine learning capabilities that automatically select the best algorithms and hyperparameters to optimize model performance. It provides specialized deep learning toolkits for tasks including i

    Jupyter Notebookautomlbig-datadata-science
    View on GitHub↗7,493
  • catboost/catboostcatboost avatar

    catboost/catboost

    8,808View on GitHub↗

    CatBoost is a gradient boosting machine learning library used to train decision tree ensembles for regression, classification, and ranking tasks. It functions as a high-performance framework that provides a categorical data processor for transforming non-numeric features, a distributed trainer for large-scale datasets, and GPU acceleration to speed up model construction. The library distinguishes itself through native handling of categorical data and text features, removing the need for manual encoding. It includes a specialized model interpretability tool that leverages SHAP values and featu

    C++big-datacatboostcategorical-features
    View on GitHub↗8,808
See all 14 alternatives to Grf→

Frequently asked questions

What does grf-labs/grf do?

Generalized Random Forests

What are the main features of grf-labs/grf?

The main features of grf-labs/grf are: Decision Tree Models.

What are some open-source alternatives to grf-labs/grf?

Open-source alternatives to grf-labs/grf include: cerlymarco/linear-tree. dmlc/xgboost — XGBoost is a distributed machine learning library for implementing scalable gradient boosting decision trees used for… h2oai/h2o-3 — h2o-3 is a distributed machine learning platform and automated machine learning framework designed for training and… jakecoltman/bartpy. manifoldai/merf. catboost/catboost — CatBoost is a gradient boosting machine learning library used to train decision tree ensembles for regression,…