Blog Post

Visualizing ML model performance

TinyML, Visualization

Jan Jongboom

January 14, 2021

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.

Showing exactly which data samples are misclassified in the feature explorer

If you haven't used the feature explorer before: it's one of the most interesting options in the Edge Impulse. The axes are the output of the signal processing process (we heavily rely on signal processing to extract interesting features beforehand, making smaller and more reliable ML models), and they can let you quickly validate whether your data separates nicely. In addition the feature explorer is integrated in Live classification, where you can compare incoming test data directly with your training set (more here).

Redesign of the neural network pages

This work has been part of a redesign of our neural network pages. These pages are now more compact, giving you full insight in both your neural network architecture, and the training performance - and giving you an easy way to compare models with different optimization options (like comparing an int8 quantized model vs. an unoptimized model) and show accurate on-device performance metrics for a wide variety of targets.


Read the Next steps sectionNext steps

Currently the feature explorer shows the performance of your training set, but over the next weeks we'll also integrate the feature explorer and the new confusion matrix to the Model testing page in Edge Impulse. This will give you direct insight in the performance of your test set in the same way, so keep an eye out for that!


Want to try the new feature explorer out? Just head to any neural network block in your Edge Impulse project and retrain. Don't have a project yet?! Follow one of our tutorials on building embedded machine learning models on real sensor data, it takes 30 minutes and you can even use your phone as a sensor.




Jan Jongboom is the CTO and cofounder of Edge Impulse. He loves pretty pictures, colors, and insight in his ML models.


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