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|GNU General Public License v3.0 or later||BSD 3-clause "New" or "Revised" License|
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[D] Best methods for imbalanced multi-class classification with high dimensional, sparse predictors
2 projects | reddit.com/r/MachineLearning | 19 Jul 2021
The best method i've seen for dealing with this bias is to create "artificial contrasts" by including possibly many permutated copies of each feature and then doing a statistical test of the random forest importance values for each feature vs its shuffled contrasts. This method is described here: https://www.jmlr.org/papers/volume10/tuv09a/tuv09a.pdf and there is an implementation here: https://github.com/ryanbressler/CloudForest
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Tracking mentions began in Dec 2020.
What are some alternatives?
sklearn - bits of sklearn ported to Go #golang
go-galib - Genetic Algorithms library written in Go / golang
gago - :four_leaf_clover: Evolutionary optimization library for Go (genetic algorithm, partical swarm optimization, differential evolution)
tfgo - Tensorflow + Go, the gopher way
gorse - An open source recommender system service written in Go
gobrain - Neural Networks written in go
Gorgonia - Gorgonia is a library that helps facilitate machine learning in Go.
shield - Bayesian text classifier with flexible tokenizers and storage backends for Go
GoLearn - Machine Learning for Go