mlgo
Machine Learning with Go (golang) Session Material for GOLAB 2018 (by miku)
randomforest
Random Forest implementation in golang (by malaschitz)
mlgo | randomforest | |
---|---|---|
1 | 2 | |
16 | 39 | |
- | - | |
0.0 | 2.6 | |
about 1 year ago | 2 months ago | |
Makefile | Go | |
MIT License | Apache License 2.0 |
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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.
mlgo
Posts with mentions or reviews of mlgo.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-02-06.
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.
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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