Feb 6Liked by Sebastian Raschka, PhD

Very clear explanations throughout the article. Thank you

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One interesting approach that I have come across for Active Learning is Label Dispersion. It's a good way of quantifying model uncertainity. TL;DR- Have a model predict an input's class a bunch of times. If it gets different outputs each time, your model is unsure. Turns out this works a lot better than using confidence.

The original paper introduced this idea in their paper- When Deep Learners Change Their Mind: Learning Dynamics for Active Learning- https://arxiv.org/abs/2107.14707

My breakdown of the paper- https://medium.com/mlearning-ai/evaluating-label-dispersion-is-it-the-best-metric-for-evaluating-model-uncertainty-e4a2b52c7fa1

This idea works for classification, but I've had success expanding it for regression as well. The process is simple- use an ensemble of diverse models, and their spread is the uncertainity of your prediction. You can take it a step further, and use probabilistic models + multiple inferences for more thoroughness.

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