Random Forest
Showing 26 of 26 projects
An open-source, in-memory platform for distributed and scalable machine learning with support for a wide range of algorithms and big data technologies.
An Automated Machine Learning Python package for tabular data with feature engineering, hyperparameter tuning, explanations, and automatic documentation.
A curated collection of research papers on decision, classification, and regression trees with implementations from top ML conferences.
A minimal benchmark comparing scalability, speed, and accuracy of popular open-source machine learning libraries for binary classification.
Python implementation of the Boruta all-relevant feature selection method with scikit-learn compatibility.
A curated list of resources for random forest and other tree-based machine learning methods.
A versatile Bayesian optimization package for hyperparameter optimization of machine learning algorithms.
A curated collection of gradient boosting research papers with implementations from top machine learning conferences.
A Ruby machine learning library with a Scikit-Learn-like interface for classification, regression, clustering, and dimensionality reduction.
A cross-platform C++ machine learning library for real-time gesture recognition with support for classification, regression, and clustering.
A universal model exchange and serialization format for decision tree forests, enabling cross-platform deployment.
A fast implementation of random forests for classification, regression, and survival analysis, optimized for high-dimensional data.
Fast, flexible, multi-threaded ensembles of decision trees for machine learning in pure Go.
A fast GPU-accelerated library for training Gradient Boosting Decision Trees (GBDT) and Random Forests.
A TensorFlow library for training, serving, and interpreting decision forest models like Random Forests and Gradient Boosted Trees.
A TypeScript machine learning library for the web and Node.js with a simple, consistent API.
A lightweight Python decision tree framework supporting ID3, C4.5, CART, CHAID, regression trees, gradient boosting, random forest, and AdaBoost with categorical feature support.
A Python library for evaluating binary classifiers in machine learning ensembles using Shapley value computation and approximation methods.
A Node.js library implementing Decision Tree (ID3/CART), Random Forest, and XGBoost algorithms with TypeScript support and automatic data type detection.
A Julia library providing a consistent API for common machine learning algorithms, designed for practitioners working with in-memory datasets.
A collection of scripts for training random forests and sparse filtering models on Kaggle datasets.
A parallel Random Forest implementation in Go for classification and regression tasks.
A Go library for scoring machine learning models using PMML, supporting neural networks, decision trees, random forests, and gradient boosted models.
A comprehensive Java library for statistics, data mining, and machine learning with interactive notebook support.
A Ruby gem for scoring predictive models using PMML, supporting decision trees, naive Bayes, logistic regression, random forests, and gradient boosted trees.
A Clojure library providing machine learning algorithms with simple APIs for data preprocessing and modeling.
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