)        if eval_res["value"] == "A":            preferences.append(a)        elif eval_res["value"] == "B":            preferences.append(b)        else:            preferences.append(None)  # No preference    return preferencespreferences = predict_preferences(dataset, results)Print out the ratio of preferences.from collections import Countername_map = {    "a": "OpenAI Functions Agent",    "b": "Structured Chat Agent",}counts = Counter(preferences)pref_ratios = {k: v / len(preferences) for k, v in counts.items()}for k, v in pref_ratios.items():    print(f"{name_map.get(k)}: {v:.2%}")    OpenAI Functions Agent: 90.00%    Structured Chat Agent: 10.00%Estimate Confidence Intervalsâ€‹The results seem pretty clear, but if you want to have a better sense of how confident we are, that model "A" (the OpenAI Functions Agent) is the preferred model, we can calculate confidence intervals. Below, use the Wilson score to estimate the confidence interval.from math import sqrtdef wilson_score_interval(    preferences: list, which: str = "a", z: float = 1.96) -> tuple:    """Estimate the confidence interval using the Wilson score.    See: https://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval#Wilson_score_interval    for more details, including when to use it and when it should not be used.    """    total_preferences = preferences.count("a") +