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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