You are evaluating a completed binary classification machine learning model. You need to use the precision as the evaluation metric. Which visualization should you use?
The confusion matrix contains the model's TP(True Positives), FN(False Negatives), FP(False Positives) and TN(True Negatives) so we can compute the precision as TP/models_positives=TP/(TP+FP).
given answer is correct
https://docs.microsoft.com/en-us/azure/machine-learning/classic/evaluate-model-performance#inspecting-the-evaluation-results-1
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