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Emerging Topics Community: Return to Trees, Part 4: Gradient Boosting Machines

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Content provided by Society of Actuaries (SOA). All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Society of Actuaries (SOA) or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ro.player.fm/legal.

In the final episode of this mini-series, Shea and Anders cover the other common tree-based ensemble model, the Gradient Boosting Machine. Like Random Forests, GBMs make use of a large number of decision trees, but they use a “boosting” approach that cleverly makes use of “weak learners” to incrementally extract information from the data. After an explanation of how GBMs work, we compare them to Random Forests and go over a few examples where they have used GBMs in their own work.

  continue reading

190 episoade

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Manage episode 415704769 series 30328
Content provided by Society of Actuaries (SOA). All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Society of Actuaries (SOA) or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ro.player.fm/legal.

In the final episode of this mini-series, Shea and Anders cover the other common tree-based ensemble model, the Gradient Boosting Machine. Like Random Forests, GBMs make use of a large number of decision trees, but they use a “boosting” approach that cleverly makes use of “weak learners” to incrementally extract information from the data. After an explanation of how GBMs work, we compare them to Random Forests and go over a few examples where they have used GBMs in their own work.

  continue reading

190 episoade

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