What is the curse of dimensionality and how does it affect machine learning models?
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A
High-dimensional data requires exponentially more training examples to maintain statistical coverage of the feature space
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B
Models with many hyperparameters always overfit regardless of training data size
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C
Adding more features always improves model performance due to increased information
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D
Deep learning models cannot process inputs with more than 1,000 features