FAQ: Random Forests - Bagging

This community-built FAQ covers the “Bagging” exercise from the lesson “Random Forests”.

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This exercise can be found in the following Codecademy content:

Data Science

Machine Learning

FAQs on the exercise Bagging

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The way we implemented the random numbers here does not guarantee that the rows we select in one set will be unique. Does that matter at all?

Why is it okay to use replacement? We’re creating fake subsets, so to speak, and will be training the model (in all likelihood) using non-factual duplicate datapoints.

How should that improve our predictive power compared to if we just split the dataset without replacement?

I guess if we split the data then we wont have enough different trees in a forest. Correct me if I am wrong.