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Core
Label Noise and Weak Supervision
Label noise is errors in your training labels, and it caps the accuracy a model can reach no matter how good the architecture is. Weak supervision is the practice of generating training labels programmatically (labeling functions, distant supervision) instead of by hand, trading some accuracy for scale. Applied AI interviews probe this because real datasets are noisy, the difference between a model stuck at 78 percent and one at 90 percent is often the labels and not the model, and candidates who understand confident learning and clean test sets are the ones who can actually move metrics.
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PRACTICE THIS IN REAL QUESTIONS
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