Episode 4 - Ensemble models. Bagging and boosting

Episode 4 of the Your Data Teacher Podcast podcast, hosted by Your Data Teacher, titled "Episode 4 - Ensemble models. Bagging and boosting" was published on June 10, 2021 and runs 11 minutes.

June 10, 2021 ·11m · Your Data Teacher Podcast

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Summary

In this episode, I'm going to talk about ensemble models, particularly bagging and boosting. Bagging is very useful for reducing variance, boosting is used for reducing bias. The most common bagging algorithm is Random Forest, the most common boosting algorithm is Gradient Boosting, whose most common implementations are XGBoost, LightGBM and CatBoost. Home Page: https://www.yourdatateacher.com

Episode Description

In this episode, I'm going to talk about ensemble models, particularly bagging and boosting. Bagging is very useful for reducing variance, boosting is used for reducing bias. The most common bagging algorithm is Random Forest, the most common boosting algorithm is Gradient Boosting, whose most common implementations are XGBoost, LightGBM and CatBoost.

Home Page: https://www.yourdatateacher.com

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