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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def kfold(dataset, problem, method, k=10):
 """ Does a k-fold on `dataset` with `method`. This is, it randomly creates k-partitions of the dataset, and k-times trains the method with k-1 parts and runs it with the partition left. After all this, returns the overall success ratio. """


    if k <= 1:
        raise ValueError("k argument must be at least 2")

    dataset = list(dataset)
    random.shuffle(dataset)

    trials = 0
    positive = 0
    for i in range(k):
        train = [x for j, x in enumerate(dataset) if j % k != i]
        test = [x for j, x in enumerate(dataset) if j % k == i]
        classifier = method(train, problem)
        for data in test:
            trials += 1
            result = classifier.classify(data)
            if result is not None and result[0] == problem.target(data):
                positive += 1

    return float(positive) / float(trials)