CloudForest
Ensembles of decision trees in go/golang. (by ryanbressler)
GoLearn
Machine Learning for Go (by sjwhitworth)
| CloudForest | GoLearn | |
|---|---|---|
| 4 | 3 | |
| 744 | 9,373 | |
| 0.0% | 0.0% | |
| 0.0 | 0.0 | |
| over 3 years ago | over 1 year ago | |
| Go | Go | |
| GNU General Public License v3.0 or later | MIT License |
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.
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.
CloudForest
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
I implemented something like this in a [pre xgboost boosting framework](https://github.com/ryanbressler/CloudForest) ~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
Personally, my Go-to ML tool for tabular data is here: https://github.com/ryanbressler/CloudForest
-
[D] Best methods for imbalanced multi-class classification with high dimensional, sparse predictors
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: https://www.jmlr.org/papers/volume10/tuv09a/tuv09a.pdf and there is an implementation here: https://github.com/ryanbressler/CloudForest
GoLearn
Posts with mentions or reviews of GoLearn.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-02-06.
- Machine Learning
- GoLang AI/ML open source projects
-
Multi-layer neural network from scratch in Go
If you're curious about a more robust machine learning library in Go, https://github.com/sjwhitworth/golearn is a quite feature-rich option.
What are some alternatives?
When comparing CloudForest and GoLearn you can also consider the following projects:
gago - :four_leaf_clover: Evolutionary optimization library for Go (genetic algorithm, partical swarm optimization, differential evolution)
Gorgonia - Gorgonia is a library that helps facilitate machine learning in Go.
tfgo - Tensorflow + Go, the gopher way
gosseract - Go package for OCR (Optical Character Recognition), by using Tesseract C++ library
gobrain - Neural Networks written in go
goml - On-line Machine Learning in Go (and so much more)