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Data Scientist & Machine Learning Interview Preparation
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Are you taking prep for your upcoming interview?
Let’s check your basic knowledge of AdaBoost. Here are 10 Q&A for your next interview.
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11.
Why is the sign function used in the final prediction of an AdaBoost model?
(A) To convert the weighted sum of weak learner outputs into a binary class label.
(B) To ensure that the final probability is always between 0 and 1.
(C ) To normalize the weights of the weak learners so they sum to one.
(D) To penalize incorrect predictions exponentially during the testing phase.
Correct Answer: (A) To convert the weighted sum of weak learner outputs into a binary class label.
AdaBoost combines predictions from all weak learners using a weighted sum. Each learner votes with a strength based on its accuracy. The final result can be positive or negative, so the sign function converts this value into a binary class label:
Positive sign → +1 class
Negative sign → -1 class
So, the sign function is used to turn the…