sklearn
bits of sklearn ported to Go #golang (by pa-m)
randomforest
Random Forest implementation in golang (by malaschitz)
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| sklearn | randomforest | |
|---|---|---|
| 0 | 2 | |
| 329 | 34 | |
| - | - | |
| 0.0 | 0.0 | |
| over 3 years ago | over 1 year ago | |
| Go | Go | |
| MIT License | Apache License 2.0 |
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.
sklearn
Posts with mentions or reviews of sklearn.
We have used some of these posts to build our list of alternatives
and similar projects.
We haven't tracked posts mentioning sklearn yet.
Tracking mentions began in Dec 2020.
randomforest
Posts with mentions or reviews of randomforest.
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
I did end up writing and using a custom library for Random Forest (it's also in AwesomGo) in one real-world project (detecting Alzheimer's and Parkinson's from speech from a mobile app) - https://github.com/malaschitz/randomForest I had better results than the team who used TensorFlow and most importantly I didn't have to use any other technology than Go. For NN's it's probably best to use https://gorgonia.org/ - but it's not exactly a user friendly library. But there is a whole book on it - Hands-On Deep Learning with Go.
What are some alternatives?
When comparing sklearn and randomforest you can also consider the following projects:
GoLearn - Machine Learning for Go
libsvm - libsvm go version
go-fann - Go bindings for FANN, library for artificial neural networks
Gorgonia - Gorgonia is a library that helps facilitate machine learning in 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
gorse - Gorse open source recommender system engine
go-cluster - k-modes and k-prototypes clustering algorithms implementation in Go
neat
CloudForest - Ensembles of decision trees in go/golang.
goml - On-line Machine Learning in Go (and so much more)