Answer: pos_label

The correct answer is **C. pos_label**. This parameter is specifically used to denote the label representing the positive class in a classification task. It's vital for the accurate calculation of metrics like precision and recall, which depend on distinguishing between positive and negative classes. - **primary_metric** is used to define the main metric AutoML aims to optimize during training, not to specify the positive class. - **max_trials** determines the maximum number of different models AutoML will experiment with, unrelated to class labeling. - **time_col** is relevant for time series tasks, indicating the column with timestamps, and doesn't involve class definitions. **Key Insights:** - Correctly setting **pos_label** ensures AutoML accurately computes and reports metrics crucial for evaluating classification models, including precision, recall, accuracy, and F1-score. - **Precision** assesses the proportion of true positives among predicted positives. - **Recall** evaluates the proportion of true positives correctly identified from all actual positives. - **Accuracy** measures the overall rate of correct classifications. - **F1-score** harmonizes precision and recall into a unified metric, offering a balanced view of model performance.

Author: LeetQuiz Editorial Team