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

Actual exam question for Databricks's Databricks Certified Professional Data Scientist exam
Question #: 44
Topic #: 3
[All Databricks Certified Professional Data Scientist Questions]

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...

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Suggested Answer: C

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

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Anastacia
3 months ago
So, L1 is just the sum of weights? That's interesting!
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Rozella
3 months ago
Yup, L2 helps with overfitting, good stuff!
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Lea
4 months ago
Wait, I thought L1 was for sparsity?
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Colton
4 months ago
Totally agree, L1 and L2 have different effects on sparsity!
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Otis
4 months ago
L2 is the sum of the square of the weights, right?
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Rosalyn
4 months ago
I believe the correct answer is A, since L2 is indeed the sum of the squares of the weights. I remember that from our last review session!
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Mitsue
4 months ago
I feel like I might confuse L1 and L2. I think L2 is about squaring the weights, but I can't recall the exact details.
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Eleonore
5 months ago
I remember practicing a question where L1 led to sparse solutions, but I’m not sure if that’s the main difference.
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Albina
5 months ago
I think L1 regularization is the sum of the absolute values of the weights, while L2 is the sum of the squares. I hope I remember that correctly!
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Johnathon
5 months ago
Hmm, this looks like a tricky one. I'll need to think through the portfolio theory concepts to figure out the right approach.
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Curt
5 months ago
I'm kind of unsure, but I think the script looks like it could be setting up an SSH connection—maybe it's related to Cisco ISE?
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Glenna
5 months ago
Wait, I'm confused. Aren't some of these tools also used in quality assurance? I'll need to double-check my understanding of the differences between quality control and quality assurance.
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Truman
5 months ago
Okay, I think I've got a handle on this. The key is determining whether the bank is liable for helping to evade the regulations.
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Devora
10 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
9 months ago
I think L3 regularization would make the model even more complex!
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Jerilyn
9 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
10 months ago
L3 regularization would be interesting, but for now we just have L1 and L2.
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Daniel
10 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
8 months ago
Thanks for the clarification, I think I'm starting to understand the difference now.
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Leota
8 months ago
Exactly! L2 regularization penalizes large weights more than L1 regularization.
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Lilli
9 months ago
So basically, L2 puts more emphasis on large weights, right?
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Fannie
9 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
9 months ago
Got it, thanks for the clarification!
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Joanna
9 months ago
Exactly, L2 penalizes large weights more heavily compared to L1.
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Katheryn
9 months ago
So, L2 focuses more on larger weights then?
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Nina
9 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
10 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
10 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
10 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
11 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
11 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
9 months ago
In practice, choosing between L1 and L2 regularization depends on the specific problem and data.
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Chara
9 months ago
L1 regularization tends to produce sparse solutions, which can be useful in feature selection.
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Wynell
9 months ago
Exactly, L2 helps prevent overfitting by penalizing large weights more than L1.
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Novella
10 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
11 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
11 months ago
I agree with Margarett, L2 regularization penalizes large weights more heavily than L1 regularization.
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Margarett
11 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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