### Mean Rank Code Implementation In the context of evaluating models, particularly in information retrieval and recommendation systems, mean rank is an important metric. Below demonstrates how one can implement this calculation within Python: ```python import numpy as np def calculate_mean_rank(true_values, predicted_ranks): """ Calculate the mean rank given true values and their corresponding ranks from predictions. Parameters: true_values (list): List containing indices or identifiers for true items. predicted_ranks (dict): Dictionary mapping item identifier to its rank among all predictions. Returns: float: The calculated mean rank. """ ranks = [] for value in true_values: if value in predicted_ranks: ranks.append(predicted_ranks[value]) else: raise ValueError(f"True value {value} not found in predicted ranks.") return np.mean(ranks) # Example usage true_items = ['item_1', 'item_2'] predicted_order_with_ranks = {'item_3': 1, 'item_1': 2, 'item_2': 3} mean_rank_score = calculate_mean_rank(true_items, predicted_order_with_ranks) print(f"The Mean Rank score is: {mean_rank_score}") ``` This function takes two parameters: `true_values`, which represents a list of correct answers; and `predicted_ranks`, which maps each answer option to its ranking position based on model prediction scores. It calculates the average rank across all provided true values by iterating over them and collecting their respective positions before computing the arithmetic mean using NumPy[^1].