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

Actual exam question for Databricks's Databricks Certified Professional Data Scientist exam

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

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Devora

3 months 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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Shanice

2 months ago

I think L3 regularization would make the model even more complex!

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Jerilyn

2 months ago

Yeah, L1 is the sum of the weights while L2 is the sum of the square of the weights.

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Shaun

2 months ago

L3 regularization would be interesting, but for now we just have L1 and L2.

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Daniel

3 months 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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Sanjuana

28 days ago

Thanks for the clarification, I think I'm starting to understand the difference now.

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Leota

1 months ago

Exactly! L2 regularization penalizes large weights more than L1 regularization.

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Lilli

1 months ago

So basically, L2 puts more emphasis on large weights, right?

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Fannie

1 months ago

Don't worry, it happens to the best of us. L2 is the sum of the square of the weights, while L1 is just the sum of the weights.

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Nada

1 months ago

Got it, thanks for the clarification!

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Joanna

2 months ago

Exactly, L2 penalizes large weights more heavily compared to L1.

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Katheryn

2 months ago

So, L2 focuses more on larger weights then?

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Nina

2 months ago

Don't worry, it happens to the best of us. L1 is the sum of the weights, while L2 is the sum of the square of the weights.

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Raylene

3 months 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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Millie

2 months ago

Don't worry, it can be confusing. The question was about the difference between L1 and L2 regularization in machine learning.

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Brandon

3 months ago

Yes, that's correct. L2 is the sum of the square of the weights, while L1 is just the sum of the weights.

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Viola

3 months 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

3 months 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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Mitzie

2 months ago

In practice, choosing between L1 and L2 regularization depends on the specific problem and data.

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Chara

2 months ago

L1 regularization tends to produce sparse solutions, which can be useful in feature selection.

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Wynell

2 months ago

Exactly, L2 helps prevent overfitting by penalizing large weights more than L1.

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Novella

3 months ago

I agree, L2 is the sum of the square of the weights, while L1 is just the sum of the weights.

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Margret

3 months 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

4 months ago

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

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Margarett

4 months 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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