How can I explain the concept of AUC to nontechnical stakeholders?

 In the context of data science, the area under the ROC curve (AUC) is a metric that is used in evaluating the performance of a binary classification model such as one used for the detection of fraudulent transactions. The ROC curve can plot the true positive rate (FPR) at various threshold settings. A higher AUC value can indicate that the model has the better discriminatory power to differentiate between positive and negative cases.

For example, let’s say you have w logistic regression model for a particular fraud detection:-

From sklearn.linear_model import LogisticRegression
From sklearn.model_selection import train_test_split
From sklearn.metrics import roc_auc_score
# Assuming X_train, X_test, y_train, y_test are your training and testing data
Model = LogisticRegression()
Model.fit(X_train, y_train)
# Predict probabilities for the test set
Probs = model.predict_proba(X_test)[:, 1]
# Calculate AUC
Auc = roc_auc_score(y_test, probs)
Print(“AUC:”, auc)