sklearn
randomforest
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| sklearn | randomforest | |
|---|---|---|
| - | 2 | |
| 343 | 39 | |
| - | - | |
| 0.0 | 2.6 | |
| almost 4 years ago | about 2 months ago | |
| Go | Go | |
| MIT License | Apache License 2.0 |
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sklearn
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Tracking mentions began in Dec 2020.
randomforest
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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.
- Boruta algorithm added to Random Forest library
What are some alternatives?
GoLearn - Machine Learning for Go
libsvm - libsvm go version
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
go-fann - Go bindings for FANN, library for artificial neural networks
goml - On-line Machine Learning in Go (and so much more)
Gorgonia - Gorgonia is a library that helps facilitate machine learning in Go.
onnx-go - onnx-go gives the ability to import a pre-trained neural network within Go without being linked to a framework or library.
gorse - Gorse open source recommender system engine
EAGO
CloudForest - Ensembles of decision trees in go/golang.