Pandas GroupBy Aggregation for Precision/Recall

You are given a classification model output dataset as a Pandas DataFrame, containing ground-truth labels and model predictions (and possibly prediction scores). Using pandas: Use groupby to aggregate the DataFrame to compute classification metrics either overall or grouped by a given dimension. Compute precision and recall for binary classification: precision = TP / (TP + FP) recall = TP / (TP + FN) Constraints / format: Must use pandas (code can be executed). No test cases are provided. Column names and grouping dimension are not specified in the post; assume at least y_true and y_pred (or equivalent) exist.