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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
What are some alternatives?
GoLearn - Machine Learning for Go
gosseract - Go package for OCR (Optical Character Recognition), by using Tesseract C++ library
Gorgonia - Gorgonia is a library that helps facilitate machine learning in Go.
gobrain - Neural Networks written in go
go-fann - Go bindings for FANN, library for artificial neural networks
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
goga - Golang Genetic Algorithm
go-galib - Genetic Algorithms library written in Go / golang
Goptuna - A hyperparameter optimization framework, inspired by Optuna.
go-pr - Pattern recognition package in Go lang.