PDE Question 89

Community Discussion

Written by other learners. Comments can disagree with the marked answer — check the official documentation when in doubt.

• Chose C1

  L1 regularization: L1 regularization encourages sparsity, effectively selecting only the most important location buckets (neighborhoods) that truly influence price. This prevents overfitting to areas with few data points and yields a more interpretable, generalizable model. And, L2 regularization is not optimal for this use case. L2 regularization shrinks weights toward zero but does not induce sparsity. When you have many location buckets (potentially hundreds or thousands), L2 will retain most of them, leading to overfitting to areas with limited data. L1 regularization is preferred because it drives irrelevant location bucket weights to exactly zero, producing a cleaner, more generalizable model.

• Chose D2

  L1 regularization (Option C) would encourage sparsity but may eliminate too many features, which can be detrimental when you need to capture subtle geographic differences

• Chose D3

  I would like choose D. L1 will ignore the irrelevant features. However, we know that lat and long are cruial for this model. We can't take away their influences. L2 helps in preventing overfitting.