randomforest
gago
| randomforest | gago | |
|---|---|---|
| 2 | 1 | |
| 46 | 882 | |
| - | - | |
| 2.6 | 3.3 | |
| 7 months ago | 6 months 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
gago
What are some alternatives?
GoLearn - Machine Learning for Go
go-galib - Genetic Algorithms library written in Go / golang
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
goga - Golang Genetic Algorithm
sklearn - bits of sklearn ported to Go #golang
gosseract - Go package for OCR (Optical Character Recognition), by using Tesseract C++ library
goml - On-line Machine Learning in Go (and so much more)
shield - Bayesian text classifier with flexible tokenizers and storage backends for Go
EAGO
CloudForest - Ensembles of decision trees in go/golang.
onnx-go - onnx-go gives the ability to import a pre-trained neural network within Go without being linked to a framework or library.
regommend - Recommendation engine for Go