xgboost-ruby
Gradient Boosting Library
High-performance machine learning library with a Ruby interface to XGBoost gradient boosting algorithm
High performance gradient boosting for Ruby
106 stars
5 watching
6 forks
Language: Ruby
last commit: almost 2 years agoLinked from 1 awesome list
machine-learningrubymlxgboost
arbox/machine-learning-with-rubyRelated projects:
| Repository | Description | Stars |
|---|---|---|
| ankane/lightgbm-ruby | A high-performance gradient boosting library for Ruby | 75 |
 paironair/xgboost-ruby | Ruby bindings for XGBoost gradient boosting library | 19 |
 dmlc/xgboost.jl | A Julia package implementing a distributed gradient boosting framework with efficient linear model solver and tree learning algorithms. | 288 |
 tqchen/xgboost | An optimized distributed gradient boosting library for machine learning | 572 |
| ankane/fasttext-ruby | Efficient text classification and representation learning library for Ruby | 204 |
| ankane/eps | A machine learning library for Ruby that allows users to build predictive models quickly and easily. | 659 |
| ankane/ngt-ruby | A high-performance approximate nearest neighbors search library for Ruby | 52 |
| ankane/torchtext-ruby | A Ruby library providing data loaders and abstractions for text and NLP tasks | 35 |
| ankane/torch.rb | A Ruby implementation of deep learning libraries inspired by PyTorch. | 739 |
| dmlc/xgboost | An optimized distributed gradient boosting library designed to be highly efficient and flexible | 26,396 |
| ankane/torchdata-ruby | A Ruby library for composable data loading and preprocessing of datasets. | 11 |
 giuse/machine_learning_workbench | A comprehensive framework for practical machine learning in Ruby. | 20 |
 kei500/liblinear-ruby | Provides an interface to train and predict with machine learning models using LIBLINEAR | 83 |
 stanfordmlgroup/ngboost | A Python library implementing a machine learning boosting framework with probabilistic prediction capabilities | 1,663 |
 chasedehan/boostaroota | An algorithm for fast feature selection using XGBoost and other tree-based classifiers | 219 |