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

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内容由Society of Actuaries (SOA)提供。所有播客内容(包括剧集、图形和播客描述)均由 Society of Actuaries (SOA) 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.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.

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Manage episode 411733083 series 30328
内容由Society of Actuaries (SOA)提供。所有播客内容(包括剧集、图形和播客描述)均由 Society of Actuaries (SOA) 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.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

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