[D] Best methods for imbalanced multi-class classification with high dimensional, sparse predictors

1. CloudForest

   1 4 744 0.0 Go

   Ensembles of decision trees in go/golang.

   

   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

2. InfluxDB

   www.influxdata.com featured

   InfluxDB – Built for High-Performance Time Series Workloads. InfluxDB 3 OSS is now GA. Transform, enrich, and act on time series data directly in the database. Automate critical tasks and eliminate the need to move data externally. Download now.

   

3. nodevectors

   2 8 548 5.6 Python

   Fastest network node embeddings in the west

   

   The best candidates for it would be UMAP or graph embedding methods

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