Model Explainability ensures you can debug or audit your machine learning models. By understanding how and why your model reacts in certain situations, you can ensure reliability and robustness, while avoiding bias.
Tell us about how you want to interpret your models and assess bias in ML.NET by taking this ~10 minute survey.
At the end, you can optionally leave your contact information if you’d like to talk with the ML.NET team about your Model Explainability and Fairness feedback.
I did the survey, but after also just watching the standup recording, had another thought. I have found it helpful to watch the "shape" of the predictions that are asked of the model, and comparing it to the shape of the data that was used to train the model. Say, for multi class classification, is the distribution of the predicted classes coming out similar to the distribution of the classes that were in the training...