How can I explain the concept of AUC to nontechnical stakeholders?
I am currently working on q particular project which is to develop a model for predicting fraudulent transactions in a q banking system. How can I explain the concept of AUC to nontechnical stakeholders and why is it an important metric for the evaluation of the performance of the fraud detection model?
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)