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:

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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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

2 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

1 months ago

D) To different scales from each other.

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Cortney

1 months ago

C) So that they are on a similar scale.

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Cory

1 months ago

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

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Ulysses

2 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

1 months ago

B) Into the normal distribution.

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Gussie

1 months ago

C) So that they are on a similar scale.

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Selma

1 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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