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CertNexus AIP-210 Exam - Topic 2 Question 29 Discussion

Actual exam question for CertNexus's AIP-210 exam

Question #: 29
Topic #: 2

[All AIP-210 Questions]

Normalization is the transformation of features:

ABy subtracting from the mean and dividing by the standard deviation.

BInto the normal distribution.

CSo that they are on a similar scale.

DTo different scales from each other.

Show Suggested Answer

Suggested Answer: C

Normalization is the transformation of features so that they are on a similar scale, usually between 0 and 1 or -1 and 1. This can help reduce the influence of outliers and improve the performance of some machine learning algorithms that are sensitive to the scale of the features, such as gradient descent, k-means, or k-nearest neighbors. Reference: [Feature scaling - Wikipedia], [Normalization vs Standardization --- Quantitative analysis]

by Jacqueline at Sep 13, 2024, 03:30 AM

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Johnna

6 months ago

C is the right answer, they need to be on a similar scale!

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Rosalind

7 months ago

I thought it was more about making everything comparable?

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Beatriz

7 months ago

Wait, isn't normalization just about the mean and std dev?

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Evan

7 months ago

Totally agree, it's key for model performance.

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Elsa

7 months ago

Normalization is all about scaling features!

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Lynda

7 months ago

I definitely remember that normalization is important for machine learning, but I’m confused about whether it’s about scaling or transforming into a normal distribution.

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Karima

8 months ago

I feel like normalization could also relate to the normal distribution, but I can't recall the exact details.

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Kanisha

8 months ago

I remember practicing a question where normalization was described as putting features on a similar scale, so I might lean towards option C.

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Terrilyn

8 months ago

I think normalization is about scaling features, but I'm not sure if it's specifically about the mean and standard deviation.

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Paris

8 months ago

Ah yes, normalization is all about centering and scaling the data, so subtracting the mean and dividing by the standard deviation sounds right to me. I'll choose A.

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Christiane

8 months ago

Hmm, I'm a bit confused on the difference between normalization and standardization. I'll have to think this through carefully.

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Shanice

8 months ago

I'm pretty sure normalization is about scaling the features to a common range, like 0 to 1, so I'll go with option C.

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Shalon

8 months ago

Okay, I remember learning that normalization transforms the features to follow a normal distribution, so I'll select B for this one.

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Ronald

8 months ago

I think I've seen this type of question before. Let me think through the options carefully.

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Serina

2 years ago

I'm feeling option C. Normalizing to a similar scale just makes good sense. Although, now I'm wondering if I should've just rolled the dice and gone with option D. Keeping things interesting, you know?

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Felicitas

2 years ago

D is the one for me. Normalizing to different scales? That's just asking for trouble. Variety is the spice of life, but not in my data!

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Jeffrey

2 years ago

D) To different scales from each other.

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Cortney

2 years ago

C) So that they are on a similar scale.

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Cory

2 years ago

A) By subtracting from the mean and dividing by the standard deviation.

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Ulysses

2 years ago

B, hands down. Transforming features into a normal distribution is where it's at. Gotta love those bell curves!

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Susy

2 years ago

B) Into the normal distribution.

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Gussie

2 years ago

C) So that they are on a similar scale.

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Selma

2 years ago

A) By subtracting from the mean and dividing by the standard deviation.

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Jesus

2 years ago

I believe it's option C, to make features on a similar scale.

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Lenny

2 years ago

Hmm, I'd go with A. Subtracting the mean and dividing by the standard deviation is the classic normalization technique, right? Keeps things nice and standardized.

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Chu

2 years ago

I would choose C as well. It's important to have features on a similar scale for accurate analysis.

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Beatriz

2 years ago

I agree with A. It helps keep everything on the same scale.

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Sina

2 years ago

Definitely, A is the classic normalization technique. It ensures consistency in the data.

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Noel

2 years ago

I think A is the way to go too. It helps in making sure all the features are on a similar scale.

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Charlene

2 years ago

I think B is also a valid option. Normalizing into a normal distribution can be useful.

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Tracey

2 years ago

Yes, you're correct. A is the classic normalization technique.

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Dalene

2 years ago

Yes, you're right! A is the correct answer. It helps in standardizing the features.

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Glendora

2 years ago

I agree with Luisa, it helps in comparing different features easily.

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Luisa

2 years ago

I think normalization is about making features on a similar scale.

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Laquanda

2 years ago

Option C is the way to go! Normalizing features to a similar scale is the way to make sure they're all playing on the same field.

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Mariann

2 years ago

Yes, it's important to have features on a similar scale for better analysis and modeling.

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Mica

2 years ago

I agree, normalizing features to a similar scale helps in comparing them accurately.

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