When analyzing whether a machine learning model works well, we rely on accuracy numbers, F1 scores and confusion matrices - but they don't give any insight into why a machine learning model misclassifies data. Is it because data looks very similar, is it because data is mislabeled, or is it because preprocessing parameters are chosen incorrectly? To answer these questions we have now added the feature explorer to all neural network blocks in Edge Impulse. The feature explorer shows your complete dataset in one 3D graph, and shows you whether data was classified correct or incorrect.
This is a companion discussion topic for the original entry at https://www.edgeimpulse.com/blog/visualizing-ml-model-performance