Boosting vs. semi-supervised learning
While gradient boosted algorithms are amazing, they aren't a silver bullet for everything. Especially when you're dealing with a dataset that only has a very small set of labels. For those use-cases you may want to resort to semi-supervised learning techniques instead. To learn more about label propagation, check the API docs here: https://scikit-learn.org/stable/modul... 00:00 Describing the edge case 01:35 When classifiers fail 04:03 Semi supervised 09:42 Applied This whiteboard video is part of the open efforts over at probabl. To learn more you can check out website or reach out to us on social media. Website: https://probabl.ai/ LinkedIn: / probabl Twitter: https://x.com/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/ If you're keen to see more videos like this, you can follow us over at @probabl_ai. #probabl
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