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Calculation formula：

• Precision TP/(TP+FP)
• Recall: TP/(TP+FN)
• F1-score： 2/(1/P+1/R)
• ROC/AUC： TPR=TP/(TP+FN), FPR=FP/(FP+TN)

ROC / AUC is the same criteria and the PR (Precision-Recall) curve (F1-score, Precision, Recall) is also the same criteria.

Real data will tend to have an imbalance between positive and negative samples. This imbalance has large effect on PR but not ROC/AUC.

So in the real world, the PR curve is used more since positive and negative samples are very uneven. The ROC/AUC curve does not reflect the performance of the classifier, but the PR curve can.

If you just do the experiment in research papers, you can use the ROC, the experimental results will be more beautiful. On another hand, PR curve use in the real problem, and it has better interpretability.

Calculation formula：

• Precision: TP/(TP+FP)
• Recall: TP/(TP+FN)
• F1-score： 2/(1/P+1/R)
• ROC/AUC： TPR=TP/(TP+FN), FPR=FP/(FP+TN)

ROC / AUC is the same criteria and the PR (Precision-Recall) curve (F1-score, Precision, Recall) is also the same criteria.

Real data will tend to have an imbalance between positive and negative samples. This imbalance has large effect on PR but not on ROC/AUC.

So in the real world, the PR curve is used more since it is common for positive and negative samples to be very uneven. The ROC/AUC curve does not reflect the performance of the classifier, but the PR curve can.

Use the ROC if you want to report experimental results in research papers, because the experimental results will be more beautiful. On the other hand, use the PR curve in a real-world problem as it has better interpretability.

Calculation formula：

• Precision： P=TP/(TP+FP)
• Recall： R=TP/(TP+FN)
• F1-score： 2/(1/P+1/R)
• ROC/AUC： TPR=TP/(TP+FN), FPR=FP/(FP+TN)

ROC / AUC is the same criteria and the PR (Precision-Recall) curve (F1-score, Precision, Recall) is also the same criteria.

Real data will face the imbalance problem, namely the imbalance between positive and negative samples. The ROC/AUC curve can remain curve, but the PR change
intensely when the testing set occurs imbalance.

So in the real world, PR curve used more actually since positive and negative samples very uneven. ROC/AUC curve does not reflect the performance of the classifier, but PRC can.

If you just do the experiment in research papers, you can use the ROC, the experimental results will be more beautiful. On another hand, PR curve use in the real problem, and it have high interpretative.

Calculation formula：

• Precision TP/(TP+FP)
• Recall: TP/(TP+FN)
• F1-score： 2/(1/P+1/R)
• ROC/AUC： TPR=TP/(TP+FN), FPR=FP/(FP+TN)

ROC / AUC is the same criteria and the PR (Precision-Recall) curve (F1-score, Precision, Recall) is also the same criteria.

Real data will tend to have an imbalance between positive and negative samples. This imbalance has large effect on PR but not ROC/AUC.

So in the real world, the PR curve is used more since positive and negative samples are very uneven. The ROC/AUC curve does not reflect the performance of the classifier, but the PR curve can.

If you just do the experiment in research papers, you can use the ROC, the experimental results will be more beautiful. On another hand, PR curve use in the real problem, and it has better interpretability.

Calculation formula：

• Precision：P=TP/(TP+FP)
• Recall：R=TP/(TP+FN)
• F1-score：2/(1/P+1/R)
• ROC/AUC：TPR=TP/(TP+FN), FPR=FP/(FP+TN)

ROC / AUC is the same criteria and PR (Precision-Recall) curve(F1-score, Precision, Recall) is also the same criteria.

Real data will face the imbalance problem, namely the imbalance between positive and negative samples. The ROC/AUC curve can remain curve, but the PR change
intensely when the testing set occurs imbalance.

So in the real world, PR curve used more actually since positive and negative samples very uneven. ROC/AUC curve does not reflect the performance of the classifier, but PRC can.

If you just do the experiment in research papers, you can use the ROC, the experimental results will be more beautiful. On another hand, PR curve use in the real problem, and it have high interpretative.

Calculation formula：

• Precision： P=TP/(TP+FP)
• Recall： R=TP/(TP+FN)
• F1-score： 2/(1/P+1/R)
• ROC/AUC： TPR=TP/(TP+FN), FPR=FP/(FP+TN)

ROC / AUC is the same criteria and the PR (Precision-Recall) curve  (F1-score, Precision, Recall) is also the same criteria.

Real data will face the imbalance problem, namely the imbalance between positive and negative samples. The ROC/AUC curve can remain curve, but the PR change
intensely when the testing set occurs imbalance.

So in the real world, PR curve used more actually since positive and negative samples very uneven. ROC/AUC curve does not reflect the performance of the classifier, but PRC can.

If you just do the experiment in research papers, you can use the ROC, the experimental results will be more beautiful. On another hand, PR curve use in the real problem, and it have high interpretative.