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PR Curve vs ROC Curve

 ROC Curves and ROC AUC

An ROC curve (or receiver operating characteristic curve) is a plot that summarizes the performance of a binary classification model on the positive class.

The x-axis indicates the False Positive Rate and the y-axis indicates the True Positive Rate.

  • ROC Curve: Plot of False Positive Rate (x) vs. True Positive Rate (y).

 

 The true positive rate is referred to as the sensitivity or the recall.

We can plot a ROC curve for a model in Python using the roc_curve() scikit-learn function.

The AUC for the ROC can be calculated in scikit-learn using the roc_auc_score() function.

 

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Precision-Recall Curves and AUC

Precision is a metric that quantifies the number of correct positive predictions made.

It is calculated as the number of true positives divided by the total number of true positives and false positives.

  • Precision = TruePositives / (TruePositives + FalsePositives)

The result is a value between 0.0 for no precision and 1.0 for full or perfect precision.

It is calculated as the number of true positives divided by the total number of true positives and false negatives (e.g. it is the true positive rate).

  • Recall = TruePositives / (TruePositives + FalseNegatives)

The result is a value between 0.0 for no recall and 1.0 for full or perfect recall.

… precision and recall make it possible to assess the performance of a classifier on the minority class.

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