Boosting and Machine Learning
True or False: Boosting improves the performance of weak classifiers by combining them into a stronger, more accurate classifier.
True
What happens after multiple rounds of boosting?
A. The weak learners are discarded.
What happens after multiple rounds of boosting?
A. The weak learners are discarded.
B. The final classifier is generated by combining the weak learners, typically through weighted voting.
C. All examples are given equal weight in each round.
D. The error rate stops improving after the first round.
C. All examples are given equal weight in each round.
D. The error rate stops improving after the first round.
B. The final classifier is generated by combining the weak learners, typically through weighted voting.
True or False: Overfitting refers to the phenomenon where a model becomes too simple to explain the data accurately.
False