randomforest
mlgo
| randomforest | mlgo | |
|---|---|---|
| 2 | 1 | |
| 39 | 16 | |
| - | - | |
| 2.6 | 0.0 | |
| 2 months ago | about 1 year ago | |
| Go | Makefile | |
| 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
mlgo
What are some alternatives?
GoLearn - Machine Learning for Go
goml - On-line Machine Learning in Go (and so much more)
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
mab - Library for multi-armed bandit selection strategies, including efficient deterministic implementations of Thompson sampling and epsilon-greedy.
sklearn - bits of sklearn ported to Go #golang
spaGO - Self-contained Machine Learning and Natural Language Processing library in Go
EAGO
dataframe-go - DataFrames for Go: For statistics, machine-learning, and data manipulation/exploration
onnx-go - onnx-go gives the ability to import a pre-trained neural network within Go without being linked to a framework or library.
Gorgonia - Gorgonia is a library that helps facilitate machine learning in Go.