Machine Learning and Data Science Quiz

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5 Questions

What are some limitations of decision tree models?

They have a tendency to overfit and capture complex decision boundaries with small trees

Which of the following is NOT an ensemble model?

Bayes Classifier

What is the purpose of using ensemble models in machine learning?

To improve prediction accuracy by combining multiple models

Which course's slides are referenced in the lecture?

CS109A Introduction to Data Science by Pavlos Protopapas

What problem do large decision trees often face?

Overfitting and capturing complex decision boundaries

Study Notes

Decision Tree Models

  • Decision tree models have limitations, including:

Ensemble Models

  • An ensemble model is not a:
    • Single decision tree (as it is a type of ensemble model)
  • The purpose of ensemble models is to:
    • Improve the accuracy and robustness of predictions by combining multiple models
  • Ensemble models are used in machine learning to:
    • Reduce overfitting and improve generalization

Lecture Slides

  • The lecture references slides from the:
    • Machine Learning course

Test your knowledge with this quiz covering ensemble models like Bagging, Random Forest, and Boosting, as well as an introduction to Bayes Classifier. The quiz also includes references to learning resources for further study.

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