randomforest
goRecommend
randomforest | goRecommend | |
---|---|---|
2 | - | |
39 | 203 | |
- | - | |
2.6 | 0.0 | |
2 months ago | almost 10 years ago | |
Go | Go | |
Apache License 2.0 | MIT License |
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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
goRecommend
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Tracking mentions began in Dec 2020.
What are some alternatives?
GoLearn - Machine Learning for Go
go-pr - Pattern recognition package in Go lang.
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
neural-go - A multilayer perceptron network implemented in Go, with training via backpropagation.
sklearn - bits of sklearn ported to Go #golang
gobrain - Neural Networks written in go
goml - On-line Machine Learning in Go (and so much more)
Varis - Golang Neural Network
EAGO
godist - Probability distributions and associated methods 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.