Databricks Machine Learning Associate Exam - Topic 2 Question 27 Discussion

Actual exam question for Databricks's Databricks Machine Learning Associate exam

Question #: 27
Topic #: 2

[All Databricks Machine Learning Associate Questions]

A team is developing guidelines on when to use various evaluation metrics for classification problems. The team needs to provide input on when to use the F1 score over accuracy.

Which of the following suggestions should the team include in their guidelines?

AThe F1 score should be utilized over accuracy when the number of actual positive cases is identical to the number of actual negative cases.

BThe F1 score should be utilized over accuracy when there are greater than two classes in the target variable.

CThe F1 score should be utilized over accuracy when there is significant imbalance between positive and negative classes and avoiding false negatives is a priority.

DThe F1 score should be utilized over accuracy when identifying true positives and true negatives are equally important to the business problem.

Show Suggested Answer

Suggested Answer: D

If the new solution requires that each of the three models computes a prediction for every record, the time efficiency during inference will be reduced. This is because the inference process now involves running multiple models instead of a single model, thereby increasing the overall computation time for each record.

In scenarios where inference must be done by multiple models for each record, the latency accumulates, making the process less time efficient compared to using a single model.

Model Ensemble Techniques

by Omer at Mar 23, 2025, 10:38 AM

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Kerry

4 months ago

Definitely use F1 when false negatives matter more!

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Simona

4 months ago

F1 over accuracy for multi-class problems? Not sure about that.

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Lucy

4 months ago

Wait, isn't accuracy fine if classes are balanced?

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Susana

4 months ago

Totally agree, accuracy can be misleading in those cases.

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Cheryl

5 months ago

F1 score is best when there's class imbalance!

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Steffanie

5 months ago

I thought the F1 score was important when we have multiple classes, but I’m not confident that’s the main reason to choose it over accuracy.

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Santos

5 months ago

I feel like the F1 score is more relevant when we have a significant class imbalance, especially if we want to minimize false negatives.

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Marget

5 months ago

I think we practiced a question where the F1 score was favored when false negatives were a concern, but I can't recall the exact details.

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Yolande

5 months ago

I remember discussing how the F1 score is particularly useful when dealing with imbalanced datasets, but I'm not sure if that's the only time to use it.

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Jamal

6 months ago

This seems straightforward. The F1 score is better than accuracy when the positive and negative classes are roughly equal in size. Accuracy can be misleading in those cases.

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Dong

6 months ago

Okay, I think I've got this. The key is that the F1 score is better when you care more about the balance between precision and recall, like when false negatives are a big deal. Accuracy doesn't capture that nuance.

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Aretha

6 months ago

Hmm, I'm a bit unsure about this one. I think the F1 score is better when there are more than two classes, but I'm not sure if that's the full story. I'll have to think it through carefully.

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Felicitas

6 months ago

I'm pretty confident I know the answer to this one. The F1 score is better than accuracy when there's an imbalance between positive and negative classes and avoiding false negatives is important.

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Michell

11 months ago

Haha, Cyril's analogy is spot on! It's like using a speedometer to judge a Formula 1 driver's performance. The F1 score is the true measure of a model's prowess in these tricky situations.

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Cyril

11 months ago

Option C all the way! Accuracy is overrated - it's like asking a goalkeeper to focus on their uniform instead of the goal. The F1 score is the real MVP when it comes to imbalanced classes.

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Lino

10 months ago

It's all about prioritizing avoiding false negatives in those situations.

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Elsa

10 months ago

Accuracy can be misleading in those cases, so the F1 score is a better choice.

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Zena

10 months ago

I agree, option C is definitely the way to go when dealing with imbalanced classes.

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Isabelle

11 months ago

Hmm, I was thinking B, but I can see how C would be more relevant. After all, who wants a model that's great at identifying true negatives but can't spot the true positives? The F1 score keeps things balanced.

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Eric

10 months ago

Definitely, C is the way to go. It's all about finding that balance between true positives and true negatives.

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Lashunda

11 months ago

Yeah, C seems like the best choice here. Balancing the trade-off between precision and recall is crucial.

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Chandra

11 months ago

I agree, C makes more sense. It's important to prioritize avoiding false negatives in certain cases.

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Hayley

12 months ago

I agree with Graciela. The F1 score gives a better sense of how the model is performing on both precision and recall, which is crucial when you've got an imbalanced dataset.

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Hannah

10 months ago

Definitely, identifying true positives and true negatives equally is key for certain business problems.

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Minna

11 months ago

Agreed, the F1 score provides a good balance between precision and recall.

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Jesus

11 months ago

I think option C is the way to go. It's important to prioritize avoiding false negatives in such cases.

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Elenor

11 months ago

Absolutely, using the F1 score is essential when dealing with imbalanced datasets.

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Graciela

1 year ago

Option C seems like the best choice here. When there's a significant imbalance between positive and negative classes and avoiding false negatives is important, the F1 score is more appropriate than just looking at overall accuracy.

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