Junior interview questionAsked at C3 AI · interview write-up
Explain boosting for classification through the margin view: sequential learners, signed margins, surrogate losses, and what training margins do and don't guarantee.
Model answer
Boosting adds weak learners in sequence, each fixing current mistakes. For labels plus or minus one, the margin is label times ensemble score: positive means correct. AdaBoost minimises the exponential loss of the margin, a smooth surrogate for 0-1 error. Large training margins tighten generalization bounds but don't guarantee test accuracy, especially with noisy labels.
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