from typing import List


def binary_classification_confidence(probabilities: List[float]) -> List[float]:
    """
    Computes confidences (c) from predicted probabilities (p), following the formula:
    if p < 0.5 -> c = 2p - 1
    else -> c = 1 - 2p

    This effectively makes that values closer to absolute 0 or 1 have higher confidences, and values closer to 0.5
    have smaller confidences.

    Note that the prediction threshold is assumed fixed at 0.5.
    """
    return list(map(lambda p: 2 * p - 1 if p >= 0.5 else 1 - 2 * p, probabilities))