Define the classification performance metrics. ROC-AUC curve. What is the Precision-Recall tradeoff, when will there be no tradeoff.

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Accepted Answer

Classification performance metrics evaluate the performance of a machine learning model in predicting categorical outcomes.

ROC-AUC curve measures the tradeoff between true positive rate and false positive rate at various thresholds.
Precision-Recall tradeoff involves balancing the precision and recall of a model. Higher precision typically means lower recall and vice versa.
There will be no tradeoff in Precision-Recall when the model achieves perfect precision and recall simultaneously.
For example, in a spam email detection system, the Precision-Recall tradeoff would involve adjusting the threshold for classifying an email as spam to optimize between catching all spam emails (recall) and minimizing false positives (precision).

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