import numpy as np

def BCAS(y_true, y_pred):
    """Baseline corrected accuracy score (BCAS).

    Parameters
    ----------
    y_true : Ground truth (correct) labels.

    y_pred : Predicted labels.

    Returns
    -------
    score : float
    """
    label, count = np.unique(y_true, return_counts=True)
    most_frequent_class = label[np.argmax(count)]
    y_baseline = np.full(len(y_true), most_frequent_class)
    as_baseline = np.mean(y_true == y_baseline)
    as_predicted = np.mean(y_true == y_pred)
    return (as_predicted - as_baseline)