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CertNexus Exam AIP-210 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:

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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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Serina
2 months 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
3 months 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 months ago
D) To different scales from each other.
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Cortney
2 months ago
C) So that they are on a similar scale.
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Cory
2 months ago
A) By subtracting from the mean and dividing by the standard deviation.
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Ulysses
3 months 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 months ago
B) Into the normal distribution.
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Gussie
2 months ago
C) So that they are on a similar scale.
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Selma
2 months ago
A) By subtracting from the mean and dividing by the standard deviation.
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Jesus
3 months ago
I believe it's option C, to make features on a similar scale.
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Lenny
3 months 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 months 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 months ago
I agree with A. It helps keep everything on the same scale.
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Sina
2 months ago
Definitely, A is the classic normalization technique. It ensures consistency in the data.
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Noel
2 months 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 months ago
I think B is also a valid option. Normalizing into a normal distribution can be useful.
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Tracey
2 months ago
Yes, you're correct. A is the classic normalization technique.
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Dalene
3 months ago
Yes, you're right! A is the correct answer. It helps in standardizing the features.
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Glendora
3 months ago
I agree with Luisa, it helps in comparing different features easily.
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Luisa
3 months ago
I think normalization is about making features on a similar scale.
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Laquanda
3 months 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
3 months ago
Yes, it's important to have features on a similar scale for better analysis and modeling.
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Mica
3 months ago
I agree, normalizing features to a similar scale helps in comparing them accurately.
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