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What does AdaBoost stand for?
Which analogy is used to explain how AdaBoost works?
What is the purpose of AdaBoost in machine learning models?
What is a weak learner in AdaBoost?
How does AdaBoost handle incorrectly classified data points?
Which real-world example is mentioned as a common use of AdaBoost?
What type of data was used in the Breast Cancer dataset mentioned in the article?
Which library was used for implementing AdaBoost in the tutorial?
What does the F1-Score represent in the AdaBoost results interpretation?
What is a limitation of AdaBoost mentioned in the article?
Which classification algorithm was NOT compared to AdaBoost in the article?
What is the main advantage of AdaBoost mentioned in the article?
Which principle does AdaBoost use to adjust the importance of weak learners based on their accuracy?
In the context of AdaBoost, what is the main goal of iterative learning?
Which of the following best describes the initial weighting of data points in AdaBoost?
What aspect of AdaBoost makes it less likely to overfit compared to some other machine learning models?
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