Which evaluation metric is most appropriate for assessing a model deployed for a

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Question: Which evaluation metric is most appropriate for assessing a model deployed for a binary classification task?

Options:

Correct Answer: Accuracy

Solution:

Accuracy is a common evaluation metric for binary classification tasks, as it measures the proportion of correct predictions made by the model.

Which evaluation metric is most appropriate for assessing a model deployed for a binary classification task?

Which evaluation metric is most appropriate for assessing a model deployed for a binary classification task?

Step 1: Understand that a binary classification task involves two classes (e.g., yes/no, true/false).
Step 2: Learn that an evaluation metric helps us measure how well our model is performing.
Step 3: Recognize that accuracy is a common metric used in binary classification.
Step 4: Know that accuracy is calculated by dividing the number of correct predictions by the total number of predictions.
Step 5: Conclude that accuracy is appropriate when the classes are balanced, meaning both classes have a similar number of instances.

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