Snowflake DSA-C02 Exam - Topic 1 Question 5 Discussion

Actual exam question for Snowflake's DSA-C02 exam

Question #: 5
Topic #: 1

[All DSA-C02 Questions]

Which metric is not used for evaluating classification models?

ARecall

BAccuracy

CMean absolute error

DPrecision

Show Suggested Answer

Suggested Answer: C

The four commonly used metrics for evaluating classifier performance are:

1. Accuracy: The proportion of correct predictions out of the total predictions.

2. Precision: The proportion of true positive predictions out of the total positive predictions (precision = true positives / (true positives + false positives)).

3. Recall (Sensitivity or True Positive Rate): The proportion of true positive predictions out of the total actual positive instances (recall = true positives / (true positives + false negatives)).

4. F1 Score: The harmonic mean of precision and recall, providing a balance between the two metrics (F1 score = 2 * ((precision * recall) / (precision + recall))).

Root Mean Squared Error (RMSE)and Mean Absolute Error (MAE) are metrics used to evaluate a Regression Model. These metrics tell us how accurate our predictions are and, what is the amount of deviation from the actual values.

by Fausto at Oct 06, 2023, 06:03 AM

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Sarina

6 months ago

I thought MAE could be used in some cases, but I guess not!

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Bette

6 months ago

Yeah, precision, recall, and accuracy are the main ones.

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Elinore

6 months ago

Wait, are you sure about that?

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Iluminada

7 months ago

Totally agree, it's for regression!

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Jaclyn

7 months ago

Mean absolute error is not used for classification models.

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Samuel

7 months ago

I agree with the others, but I’m still second-guessing myself. I just hope I remember the definitions correctly during the exam!

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Joseph

7 months ago

I’m a bit confused; I thought all of these could be used in some context, but I definitely recall that mean absolute error is not typically for classification.

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Nida

7 months ago

I remember practicing questions where we had to identify metrics, and I feel like mean absolute error is more related to regression models.

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Elenor

8 months ago

I think recall, accuracy, and precision are all metrics we use for classification, but I'm not sure about mean absolute error.

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Meaghan

8 months ago

I've got this! Mean absolute error is for regression problems, not classification. The answer has to be C.

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Martina

8 months ago

This is a good question to test our understanding of model evaluation. I'll eliminate the options I'm confident about and then make an educated guess on the remaining one.

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Goldie

8 months ago

Okay, let me review the key metrics - precision, recall, F1-score. I think mean absolute error is the one that doesn't apply here.

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Evangelina

8 months ago

Hmm, this one seems tricky. I'll need to think carefully about the different metrics used for classification models.

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Youlanda

8 months ago

I'm pretty sure accuracy is not used for evaluating classification models. That's more for regression tasks, right?

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Brett

8 months ago

Okay, I remember learning about the Gordon's Growth Model in my finance class. It's a way to value a stock based on its expected future dividends. So the correct answer must be A, the Dividend Discount Model.

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Shaun

8 months ago

This feels a bit tricky; I remember it has something to do with network resources too, but I'm not sure if that's the main feature.

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Ronnie

8 months ago

This seems like a straightforward question about non-quantitative job evaluation techniques. I'll need to carefully review the options and think about which one is not appropriate for Martha to share with her team.

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