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What is bagging and boosting in Machine Learning?

What is bagging and boosting in Machine Learning?

Postby James Jones » October 26th, 2021, 4:01 am

What is bagging and boosting in Machine Learning?
James Jones
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Re: What is bagging and boosting in Machine Learning?

Postby Roberts Carter » October 26th, 2021, 4:07 am

Bagging is a technique in machine learning for collecting predictions of the same kind, i.e. those generated by the same algorithm. Consider the Random Forest, for example. Each model is produced independently and given equal weight. It solves the problem of overfitting. It also decreases variability.

Boosting is a technique for aggregating predictions from many algorithms. One example is gradient boosting. The performance of past models has a significant influence on the present model. It helps to reduce bias.
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