The PRC is a plot of the precision and recall of a model for different thresholds. Precision is the fraction of positive predictions that are actually positive, while recall is the fraction of actual positives that are correctly predicted.

The ROC is a plot of the true positive rate (TPR) and the false positive rate (FPR) of a model for different thresholds. The TPR is the fraction of actual positives that are correctly predicted, while the FPR is the fraction of actual negatives that are incorrectly predicted as positives.

The main difference between the PRC and ROC curves is that the PRC focuses on the precision of the model, while the ROC focuses on the TPR of the model.

• The PRC is more informative when the dataset is imbalanced, i.e., when the number of positive examples is much smaller than the number of negative examples. This is because the ROC curve can be misleading in imbalanced datasets, as it can be dominated by the majority class.

• The ROC is more informative when the dataset is balanced, i.e., when the number of positive examples is approximately equal to the number of negative examples. This is because the PRC curve can be difficult to interpret in balanced datasets, as it can be very flat.

Key differences between the PRC and ROC curves

Precision-Recal Curve (PRC):

•  Focus:  Precision
•  More informative with:  Imbalanced Datasets
• Interpretion : Easier to interpret in imbalanced datasets

Receiver-Operating-Characteristic-Curve (ROC):

• Focus: True Positive Rate (TPR)
• More informative with: Balanced Datasets
• Interpetation: Easier ti interpret in balanced datsets