The variable thresholds trick

There are many ways to improve a classifier, but the most inspiring way to improve it is to really think hard on how you want to apply your model. The reason is because there might just be an amazing opportunity to use variable thresholds, which can really make the model more flexible in production. 00:00 Introducing variable thresholds 01:05 Business case for variable thresholds 04:01 Understanding the maths 07:13 Towards a notebook 11:23 Measure the bonus Relevant links: https://probabl-ai.github.io/calibration-cost-sensitive-learning/intro.html https://github.com/probabl-ai/youtube-appendix/blob/main/17-dynamic-threshold/dynamic-thresholds.ipynb Website: https://probabl.ai/ LinkedIn: https://www.linkedin.com/company/probabl Twitter: https://x.com/probabl_ai Bluesky: https://bsky.app/profile/probabl.bsky.social Discord: https://discord.probabl.ai We also host a podcast called Sample Space, which you can find on your favourite podcast player. All the links can be found here: https://rss.com/podcasts/sample-space/ #probabl