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Questions and Answers
What are some limitations of decision tree models?
What are some limitations of decision tree models?
- They have a tendency to overfit and capture complex decision boundaries with small trees (correct)
- They are fast to train but have low interpretability
- They are highly interpretable and prone to underfitting
- They are slow to train and have a high bias
Which of the following is NOT an ensemble model?
Which of the following is NOT an ensemble model?
- Boosting
- Bayes Classifier (correct)
- Bagging
- Random Forest
What is the purpose of using ensemble models in machine learning?
What is the purpose of using ensemble models in machine learning?
- To create highly interpretable models
- To reduce the complexity of decision boundaries
- To improve prediction accuracy by combining multiple models (correct)
- To speed up the training process of individual models
Which course's slides are referenced in the lecture?
Which course's slides are referenced in the lecture?
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What problem do large decision trees often face?
What problem do large decision trees often face?
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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
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Description
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.
