Data visualization

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.