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Databricks Exam Databricks Certified Professional Data Scientist Topic 6 Question 75 Discussion

Actual exam question for Databricks's Databricks Certified Professional Data Scientist exam
Question #: 75
Topic #: 6
[All Databricks Certified Professional Data Scientist Questions]

A fruit may be considered to be an apple if it is red, round, and about 3" in diameter. A naive Bayes classifier considers each of these features to contribute independently to the probability that this fruit is an apple, regardless of the

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Suggested Answer: B, C

Contribute your Thoughts:

Angella
2 months ago
This question is so apple-ling, I can't even. But I'm glad I now know the correct answer, or I'd be in a real jam.
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Ellsworth
10 days ago
D: Thanks for clarifying, I was confused too.
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Yuriko
11 days ago
C: No, A is correct. Each feature is considered independently.
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Azzie
24 days ago
B: Really? I thought it was C) Presence or absence of the other features.
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Yuki
2 months ago
A: The answer is A) Presence of the other features.
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Matthew
2 months ago
Wait, so the classifier doesn't even consider if the fruit has a worm in it? That's some serious fruit discrimination if you ask me!
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Margo
1 months ago
User 2: Yeah, the classifier assumes each feature contributes independently to the probability of the fruit being an apple.
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Sabine
2 months ago
User 1: It doesn't matter if the fruit has a worm, the classifier only looks at specific features.
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Letha
2 months ago
Hmm, I was a bit unsure about this one. But now it makes sense - the classifier doesn't care if the apple is also juicy or has a stem, as long as it's red, round, and 3 inches wide.
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Malissa
2 months ago
Ah, I see! So, it's like saying the fruit's color doesn't depend on whether it's round or not. That's a pretty neat assumption for a classifier to make.
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Clorinda
2 months ago
But wouldn't the presence or absence of other features affect the probability of the fruit being an apple?
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Becky
2 months ago
The correct answer is C) Presence or absence of the other features. A naive Bayes classifier assumes that each feature contributes independently to the probability of the class variable, regardless of the presence or absence of the other features.
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Antonette
25 days ago
That's right. It assumes that each feature contributes independently to the probability of the class variable.
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Maxima
1 months ago
Oh, I see. So each feature is considered independently in a naive Bayes classifier.
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Eleni
1 months ago
Actually, the correct answer is C) Presence or absence of the other features.
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Ryan
2 months ago
I think the answer is A) Presence of the other features.
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Pansy
2 months ago
I disagree, I believe the answer is C) Presence or absence of the other features.
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Clorinda
3 months ago
I think the answer is A) Presence of the other features.
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