What does the term 'bagging' refer to in the context of Random Forests?

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Question: What does the term \'bagging\' refer to in the context of Random Forests?

Options:

Correct Answer: Combining predictions from multiple models

Solution:

Bagging refers to combining predictions from multiple models to improve the overall performance and robustness of the model.

What does the term 'bagging' refer to in the context of Random Forests?

What does the term 'bagging' refer to in the context of Random Forests?

Step 1: Understand that 'bagging' is short for 'Bootstrap Aggregating'.
Step 2: Know that it involves creating multiple versions of a model.
Step 3: Each version of the model is trained on a different random sample of the data.
Step 4: After training, each model makes its own predictions.
Step 5: The final prediction is made by combining the predictions from all models, usually by averaging or voting.
Step 6: This process helps to reduce errors and improve the overall performance of the model.

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