Ensemble Methods and Decision Trees Quiz
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What is the basic idea behind Ensemble Classifiers?

  • Relying on a complex parameter tuning process
  • Creating a transparent representation of the classifier
  • Using a single classifier for all predictions
  • Building different 'experts' and letting them vote (correct)

    • What is a disadvantage of Ensemble Classifiers?

  • It provides a compact representation of the classifier
  • It can only include one type of classifier
  • It requires minimal parameter tuning
  • The combined classifier is not so transparent (black box) (correct)

    • What is the purpose of Unweighted voting in Ensemble of Classifiers?

  • To simplify the implementation process
  • To exclude certain classifiers from the process
  • To combine the classification results from different classifiers (correct)
  • To give more weight to certain classifiers

    • What does Ensemble-based methods involve?

    <p>Creation of large set of classifiers, here called the 'base learner'</p> Signup and view all the answers

    What is an advantage of Ensemble Classifiers?

    <p>Improvement in predictive performance</p> Signup and view all the answers

    What is the main idea behind Ensemble Classifiers?

    <p>Combining classifiers to improve performance</p> Signup and view all the answers

    What is a disadvantage of Ensemble Classifiers?

    <p>The combined classifier is not so transparent (black box)</p> Signup and view all the answers

    What is the purpose of Unweighted voting in Ensemble of Classifiers?

    <p>To give each classifier equal voting power</p> Signup and view all the answers

    What does Ensemble-based methods involve?

    <p>Combining a large set of classifiers</p> Signup and view all the answers

    What is an advantage of Ensemble Classifiers?

    <p>Improvement in predictive performance</p> Signup and view all the answers

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