Boosting and Bagging

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What is the difference between Boosting and Bagging?

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Priyabrata 55 years 1 Answer 843 views Master 0

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  1. swap007
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    Bagging stands for Bootstrap aggregating. Bootstraping means creating multiple
    datasets from a single dataset by performing sampling with replacement.
    Generally, decision trees are associated with high variance. So, we build multiple
    decision trees over bootstrapped samples and average out the results which help in reducing the
    variance.
    In boosting, we start with building weak learners and then go on combining the trees to
    make one final strong learner. The observations which are misclassified in a particular
    tree, are given more weightage while building the next tree. As we go on building more
    and more trees, it leads to reduction in the error.

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