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Amazon AIF-C01 Exam - Topic 5 Question 16 Discussion

A company wants to classify human genes into 20 categories based on gene characteristics. The company needs an ML algorithm to document how the inner mechanism of the model affects the output.Which ML algorithm meets these requirements?
A) Decision trees
B) Linear regression
C) Logistic regression
D) Neural networks

Amazon AIF-C01 Exam - Topic 5 Question 16 Discussion

Actual exam question for Amazon's AIF-C01 exam
Question #: 16
Topic #: 5
[All AIF-C01 Questions]

A company wants to classify human genes into 20 categories based on gene characteristics. The company needs an ML algorithm to document how the inner mechanism of the model affects the output.

Which ML algorithm meets these requirements?

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Suggested Answer: A

by Stevie at Dec 09, 2024, 05:13 AM

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Huey
8 months ago
Totally agree, decision trees are the way to go!
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Caprice
8 months ago
Wait, can decision trees really handle 20 categories?
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Teri
8 months ago
Linear regression doesn't classify, so it's out.
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Tayna
8 months ago
I think neural networks are too complex for this.
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Mabel
9 months ago
Decision trees are great for interpretability!
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Annelle
9 months ago
Neural networks seem complex and less interpretable, so I doubt they would meet the documentation requirement.
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Emiko
9 months ago
I practiced a similar question, and I think logistic regression is more about binary outcomes, so it might not fit this case.
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Veronika
9 months ago
I'm not entirely sure, but I think linear regression is more for continuous outcomes, not classification.
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Yaeko
9 months ago
I remember that decision trees are great for interpretability, so they might be the right choice here.
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Yolande
9 months ago
Logistic regression could potentially work for the 20-class classification, but I'm not sure if it would give the same level of insight into the model's inner workings as decision trees.
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Val
9 months ago
I'm a bit unsure about this one. Linear regression seems like it might not be the best choice since the target variable is categorical, not continuous.
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Cathrine
9 months ago
Neural networks might also work for this classification task, but I'm not sure if they would provide the level of transparency the company is looking for.
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Raylene
10 months ago
I think decision trees would be a good fit here since they can handle the 20 gene categories and their inner workings are interpretable.
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Abel
2 years ago
Linear regression might not be the best fit for this, as it is more suited for predicting continuous values.
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Roxanne
2 years ago
I see both points, but I think Logistic regression could also be a good choice.
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Louis
2 years ago
I disagree, I believe Neural networks would be more suitable for this task.
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Mariann
2 years ago
I think Decision trees would be the best option for this.
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Man
2 years ago
Linear regression? Seriously? This is like trying to fit a square peg into a round hole. Neural networks are the only choice here.
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Miesha
2 years ago
Logistic regression? Nah, that's for simple binary classification. This problem calls for the big guns - neural networks!
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Emile
2 years ago
Logistic regression is not suitable for this problem, neural networks offer more flexibility.
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Laquanda
2 years ago
Linear regression is too simplistic for this kind of gene classification.
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Burma
2 years ago
I agree, decision trees might not be able to capture the intricate relationships in the data.
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Sheridan
2 years ago
Neural networks are definitely the way to go for this complex task.
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Audria
2 years ago
Decision trees? Sounds like a lumberjack solution to a genetic problem. Neural networks are the real deal.
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Launa
2 years ago
Neural networks are the way to go! They can handle the complexity of gene characteristics like a boss.
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Quentin
2 years ago
Linear regression and logistic regression are more suited for continuous data, not gene classification.
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Georgeanna
2 years ago
Decision trees might not be able to capture all the nuances of gene characteristics.
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Colette
2 years ago
I agree, they can handle the complexity of gene characteristics really well.
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Antione
2 years ago
Neural networks are definitely the best choice for classifying human genes.
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