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Which statement is true in the context of evaluating metrics for machine learning algorithms?

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Which statement is true in the context of evaluating metrics for machine learning algorithms?

A.

A random classifier has AUC (the area under ROC curve) of 0.5

B.

Using only one evaluation metric is sufficient

C.

The F-score is always equal to precision

D.

Recall of 1 (100%) is always a good result

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