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Emerging Topics Community: Return to Trees, Part 3: Random Forest

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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.

Building on the discussion of individual decision trees in the prior episode, Shea and Anders shift to one of today’s most popular ensemble models, the Random Forest. At first glance, the algorithm may seem like a brute force approach of simply running hundreds or thousands of decision trees, but it leverages the concept of “bagging” to avoid overfitting and attempt to learn as much as possible from the entire data sets, not just a few key features. We close by covering strengths and weaknesses of this model and providing some real-life examples.

  continue reading

190 episoade

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Manage episode 411733083 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.

Building on the discussion of individual decision trees in the prior episode, Shea and Anders shift to one of today’s most popular ensemble models, the Random Forest. At first glance, the algorithm may seem like a brute force approach of simply running hundreds or thousands of decision trees, but it leverages the concept of “bagging” to avoid overfitting and attempt to learn as much as possible from the entire data sets, not just a few key features. We close by covering strengths and weaknesses of this model and providing some real-life examples.

  continue reading

190 episoade

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