def METHOD_NAME(self, y_targets, y_prob_preds, threshold=0.5):
    y_hat_lbls = []
    pred_pos_count = 0
    pred_neg_count = 0
    correct_count = 0
    for y_prob, y_t in zip(y_prob_preds, y_targets):
        if y_prob <= threshold:
            pred_neg_count += 1
            y_hat_lbl = 0
        else:
            pred_pos_count += 1
            y_hat_lbl = 1
        y_hat_lbls.append(y_hat_lbl)
        if y_hat_lbl == y_t:
            correct_count += 1
    return np.array(y_hat_lbls), [pred_pos_count, pred_neg_count, correct_count]