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MMLU / Which among the following prevents overfitting when we perform bagging?

Problem

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question

Which among the following prevents overfitting when we perform bagging?

choices

  1. The use of sampling with replacement as the sampling technique
  2. The use of weak classifiers
  3. The use of classification algorithms which are not prone to overfitting
  4. The practice of validation performed on every classifier trained

subject

machine_learning

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Source and history

task_id
f177a8bf-8b96-5b6e-925b-cb674e6efdfc
task_key
all--test--f177a8bf-8b96-5b6e-925b-cb674e6efdfc
task_revision_id
1
config / split
all / test

Official source

initial import