randomForest

Random Forests

A Go implementation of random forest algorithms for machine learning and data analysis

Random Forest implementation in golang

GitHub

46 stars
5 watching
11 forks
Language: Go
last commit: 9 months ago
Linked from 2 awesome lists


Backlinks from these awesome lists:

Related projects:

Repository Description Stars
mljs/random-forest A JavaScript implementation of a random forest algorithm for classification and regression tasks. 61
masatoi/cl-random-forest An implementation of Random Forest for multiclass classification and univariate regression in Common Lisp. 59
fxsjy/rf.go An implementation of Random Forest algorithm in GoLang for classification and regression tasks. 114
karpathy/random-forest-matlab An implementation of a Random Forest algorithm in MATLAB 183
karpathy/forestjs An implementation of a Random Forest algorithm for binary classification in JavaScript. 299
ehrlinger/ggrandomforests A package for visualizing and analyzing random forest models using ggplot2 146
ryanbressler/cloudforest A high-performance ensemble learning framework for decision trees in Go. 739
tmadl/sklearn-random-bits-forest An implementation of a hybrid machine learning algorithm combining neural networks, boosting, and random forests. 9
rolnicklab/openforest A catalogue of forest monitoring datasets for machine learning and research purposes. 115
imbs-hl/ranger A fast implementation of random forests suitable for high-dimensional data in C++ 776
glouppe/phd-thesis In-depth analysis of Random Forests algorithm to improve understanding and interpretability 527
azvoleff/gfcanalysis Tools and analyses for working with Hansen et al.'s Global Forest Change dataset 17
awalterschulze/goderive Automates generating implementations of common Go functions from input parameter types. 1,243
juanb09111/finnforest A collection of data and software tools for training machine learning models to analyze and understand forests 46
raphaelcampos/stacking-bagged-boosted-forests This project presents a novel approach to classification using Random Forests and stacking techniques 6