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Snowflake Exam DSA-C02 Topic 2 Question 36 Discussion

Actual exam question for Snowflake's DSA-C02 exam
Question #: 36
Topic #: 2
[All DSA-C02 Questions]

Which of the following metrics are used to evaluate classification models?

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Suggested Answer: D

Evaluation metrics are tied to machine learning tasks. There are different metrics for the tasks of classification and regression. Some metrics, like precision-recall, are useful for multiple tasks. Classification and regression are examples of supervised learning, which constitutes a majority of machine learning applications. Using different metrics for performance evaluation, we should be able to im-prove our model's overall predictive power before we roll it out for production on unseen data. Without doing a proper evaluation of the Machine Learning model by using different evaluation metrics, and only depending on accuracy, can lead to a problem when the respective model is deployed on unseen data and may end in poor predictions.

Classification metrics are evaluation measures used to assess the performance of a classification model. Common metrics include accuracy (proportion of correct predictions), precision (true positives over total predicted positives), recall (true positives over total actual positives), F1 score (har-monic mean of precision and recall), and area under the receiver operating characteristic curve (AUC-ROC).

Confusion Matrix

Confusion Matrix is a performance measurement for the machine learning classification problems where the output can be two or more classes. It is a table with combinations of predicted and actual values.

It is extremely useful for measuring the Recall, Precision, Accuracy, and AUC-ROC curves.

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))).

These metrics help assess the classifier's effectiveness in correctly classifying instances of different classes.

Understanding how well a machine learning model will perform on unseen data is the main purpose behind working with these evaluation metrics. Metrics like accuracy, precision, recall are good ways to evaluate classification models for balanced datasets, but if the data is imbalanced then other methods like ROC/AUC perform better in evaluating the model performance.

ROC curve isn't just a single number but it's a whole curve that provides nuanced details about the behavior of the classifier. It is also hard to quickly compare many ROC curves to each other.


Contribute your Thoughts:

Francoise
1 months ago
ROC curve? More like 'Really Obvious Choice' curve. D all the way, baby!
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Mammie
1 months ago
Ah, the classic 'All of the above' trap. But in this case, it's the right choice. Gotta love a one-stop-shop for model evaluation.
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Wilda
1 months ago
Confusion matrix? I'm already confused just looking at the options. Option D is my pick.
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Gertude
9 days ago
I agree, it can be confusing at first. But it's a useful tool.
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Cherry
10 days ago
Confusion matrix is used to evaluate classification models.
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Reed
1 months ago
F1 score? More like F-bomb score, am I right? But seriously, D is the way to go.
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Annita
28 days ago
D) All of the above
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Emerson
30 days ago
C) Confusion matrix
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Lennie
1 months ago
B) F1 score
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Delisa
1 months ago
A) Area under the ROC curve
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Rocco
2 months ago
I'm not sure about the F1 score, can someone explain why it is used in classification models?
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Keith
2 months ago
I agree with Susy, all those metrics are important for evaluating classification models.
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Susy
2 months ago
I think the answer is D) All of the above.
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Lovetta
2 months ago
Definitely going with option D. Can't go wrong with all the metrics!
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Dyan
1 months ago
Option D is definitely the way to go for a thorough evaluation.
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Glendora
1 months ago
It's important to have a holistic view of the model's performance.
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Val
1 months ago
I always prefer to use all the metrics to get a complete picture of how well the classification model is performing.
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Malcolm
2 months ago
I think it's important to consider all aspects of the model's performance, so option D is the way to go.
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Hyman
2 months ago
I agree, using all the metrics gives a comprehensive evaluation of the classification model.
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Jenelle
2 months ago
I always make sure to consider all the metrics when evaluating classification models.
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Deangelo
2 months ago
I agree, using all the metrics gives a comprehensive evaluation.
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