gago VS CloudForest

Compare gago vs CloudForest and see what are their differences.


:four_leaf_clover: Evolutionary optimization library for Go (genetic algorithm, partical swarm optimization, differential evolution) (by MaxHalford)


Ensembles of decision trees in go/golang. (by ryanbressler)
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gago CloudForest
1 4
869 735
- -
0.0 0.0
about 1 year ago about 2 years ago
Go Go
MIT License GNU General Public License v3.0 or later
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.


Posts with mentions or reviews of gago. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-12-18.


Posts with mentions or reviews of CloudForest. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-09-12.
  • Trinary Decision Trees for missing value handling
    2 projects | | 12 Sep 2023
    I implemented something like this in a [pre xgboost boosting framework]( ~10 years ago and it worked well.

    It isn't even that much of a speed hit using the classical sorting CART implementation. However xgboost and ligthgbm use histogram based approximate sorting which might be harder to adapt in a performant way. And certainly the code will be a lot messier.

  • Future of Golang
    1 project | /r/golang | 22 Sep 2022
    Personally, my Go-to ML tool for tabular data is here:
  • [D] Best methods for imbalanced multi-class classification with high dimensional, sparse predictors
    2 projects | /r/MachineLearning | 19 Jul 2021
    The best method i've seen for dealing with this bias is to create "artificial contrasts" by including possibly many permutated copies of each feature and then doing a statistical test of the random forest importance values for each feature vs its shuffled contrasts. This method is described here: and there is an implementation here:

What are some alternatives?

When comparing gago and CloudForest you can also consider the following projects:

go-galib - Genetic Algorithms library written in Go / golang

libsvm - libsvm go version

goga - Golang Genetic Algorithm

gobrain - Neural Networks written in go

gosseract - Go package for OCR (Optical Character Recognition), by using Tesseract C++ library

shield - Bayesian text classifier with flexible tokenizers and storage backends for Go

m2cgen - Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies

regommend - Recommendation engine for Go

Gorgonia - Gorgonia is a library that helps facilitate machine learning in Go.