Answer: All of these

In AutoML (Automated Machine Learning) frameworks, for classification problems, multiple evaluation metrics are automatically computed for each model run to assess performance comprehensively. These include: - **Accuracy**: The proportion of true results (true positives and true negatives) among all cases. - **Area Under the ROC Curve (AUC-ROC)**: Indicates the model's ability to distinguish between classes, with 1 being perfect and 0.5 no better than random. - **Recall**: The proportion of actual positives correctly identified (sensitivity). - **F1 Score**: The harmonic mean of precision and recall, balancing the two. AutoML platforms compute these metrics to offer a detailed view of each model's performance, aiding in selecting the most suitable model based on specific application needs.

Author: LeetQuiz Editorial Team