Which consequence results when over-parameterizing a function that interpolates noisy data?

💡 Explanation

Over-parameterization leads to overfitting, because the model learns the noise present in the data by memorization, via the mechanism of increasing model complexity. Therefore high variance/low bias results, rather than capturing true patterns reducing generalization errors.

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