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Can you elaborate on the dissimilarity between L1 and L2 regularization and how it's implemented in regression analysis? Also, when would you prefer one over the other?
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Compare the mathematical formulations, then focus on the effects (L1 for feature selection/sparsity vs L2 for handling multicollinearity) with specific use cases for each approach.
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