code-kern-ai
Visualize embeddings in 2 or 3 dimensions
Is your feature request related to a problem? Please describe. Visualize data to get a better overview of "missed" spots or clusters of instances that a model got wrong.
Describe the solution you’d like Dimension reduction for easy visualization of datapoints
Describe alternatives you’ve considered -
Additional context Requested by GeorgePearse on Discord
@jens @jhoetter I think the core value of visualization of low dimensionality data is to see whether there are any clusters/classes you've completely missed so far, and if so, how large are they. Hard to understand that from the current UI design.
After the embeddings you could just have a "select dimensionality reduction" option with PCA, t-sne, and UMAP as the dimensionality reduction methods (UMAPs worked best for me in the past).
Also helps with Active Learning if you can see a cluster of instances that the model gets wrong.
