IBM Data Science Test 2025 – 400 Free Practice Questions to Pass the Exam

Question: 1 / 400

What is the main purpose of a ROC curve?

To visualize data distributions

To evaluate the performance of a regression model

To illustrate the diagnostic ability of a binary classifier

The main purpose of a ROC (Receiver Operating Characteristic) curve is to illustrate the diagnostic ability of a binary classifier. It provides a graphical representation of a classifier's performance across various threshold settings. By plotting the true positive rate (sensitivity) against the false positive rate (1-specificity), the ROC curve allows you to see how well the model can distinguish between the positive and negative classes at different thresholds.

A key component of interpreting a ROC curve is the area under the curve (AUC), which quantifies the overall ability of the model to discriminate between the two classes. A model with a higher AUC is considered better at making accurate predictions. This makes the ROC curve particularly useful in assessing classifiers in scenarios where you want to analyze the trade-offs between sensitivity and specificity.

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To compare different machine learning models

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