Variance Reduction Techniques
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Questions and Answers

What does sampling with replacement produce, according to the text?

  • Static data
  • I.I.D. data (correct)
  • Dependent data
  • Random data

    • What guarantees the following asymptotic result in the context of bagging?

  • The law of large numbers (correct)
  • The law of small probabilities
  • The central limit theorem
  • The principle of maximum likelihood

    • In practice, what does bagging effectively reduce?

  • Training time
  • Variance (correct)
  • Bias
  • Model complexity

    • What is a key advantage of using bagging, as mentioned in the text?

    <p>Reduces variance for high variance classifiers/regressors</p> Signup and view all the answers

    What is required for an unbiased classifier to produce the correct solution?

    <p>Low bias</p> Signup and view all the answers

    Which type of data does bagging work effectively with?

    <p>Independent and identically distributed data</p> Signup and view all the answers

    What does bagging reduce without increasing for an unbiased classifier?

    <p>Variance</p> Signup and view all the answers

    What type of classifiers/regressors benefit from variance reduction through bagging?

    <p>High variance classifiers/regressors</p> Signup and view all the answers

    'Bagging' stands for:

    <p>'Bootstrapping Aggregating'</p> Signup and view all the answers

    'I.I.D.' stands for:

    <p>'Independent and Identically Distributed'</p> Signup and view all the answers

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