Databricks Exam Databricks-Certified-Professional-Data-Scientist Topic 3 Question 44 Discussion

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Question #: 44
Topic #: 3

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Regularization is a very important technique in machine learning to prevent overfitting. Mathematically speaking, it adds a regularization term in order to prevent the coefficients to fit so perfectly to overfit. The difference between the L1 and L2 is...

AL2 is the sum of the square of the weights, while L1 is just the sum of the weights

BL1 is the sum of the square of the weights, while L2 is just the sum of the weights

CL1 gives Non-sparse output while L2 gives sparse outputs

DNone of the above
Regularization is a very important technique in machine learning to prevent overfitting. Mathematically speaking, it adds a regularization term in order to prevent the coefficients to fit so perfectly to overfit. The difference between the L1 and L2 is just that L2 is the sum of the square of the weights, while L1 is just the sum of the weights. As follows: L1 regularization on least squares:

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Suggested Answer: C

by Fletcher at Jul 26, 2023, 02:20 AM

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Devora

1 days ago

Regularization, the secret sauce of machine learning. I wonder if there's a version called L3 that's just the sum of the cubes. That would really spice things up!

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Daniel

5 days ago

Ah, the age-old L1 vs L2 conundrum. I feel like I should know this, but my brain is just not cooperating today. Maybe I need to do some more regularization of my own mental processes.

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Raylene

14 days ago

Hmm, looks like the difference is that L2 is the sum of the squares, and L1 is just the sum. Easy peasy, right? Wait, what was the question again?

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Viola

15 days ago

Ooh, this one's a tricky one. Gotta remember that L1 gives us a non-sparse output, while L2 is the one that gives us the sparse outputs. I better double-check my notes on that.

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Gerald

17 days ago

Ah, the age-old L1 vs L2 debate. L2 gives us the sum of the squares, while L1 is just the sum of the weights. Seems straightforward enough to me.

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Margret

19 days ago

I'm not sure, but I think L1 gives Non-sparse output while L2 gives sparse outputs. Can someone confirm?

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Ozell

23 days ago

I agree with Margarett, L2 regularization penalizes large weights more heavily than L1 regularization.

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Margarett

25 days ago

I think the answer is A) L2 is the sum of the square of the weights, while L1 is just the sum of the weights.

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