def METHOD_NAME(model, dataset):
    """The model should work fine when features disappear."""
    from river import utils
    for x, y in dataset:
        features = list(x.keys())
        random.shuffle(features)
        if utils.inspect.isanomalydetector(model):
            model.score_one({i: x[i] for i in features[:-3]})  # drop 3 features at random
            model.learn_one(x)
        else:
            model.predict_one({i: x[i] for i in features[:-3]})
            model.learn_one(x, y)