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

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Indhold leveret af Society of Actuaries (SOA). Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af Society of Actuaries (SOA) eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.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.

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190 episoder

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Manage episode 415704769 series 30328
Indhold leveret af Society of Actuaries (SOA). Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af Society of Actuaries (SOA) eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.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 episoder

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