设计天气数据的分类问题
在普通的二类或多类分类问题中，我们可以使用任何著名的机器学习算法（例如朴素贝叶斯或支持向量机）来训练和测试模型。 我的问题是，我收到了天气数据，其中标签变量的格式为“20% 雨，80% 干燥”或“30% 多云，70% 雨”等。 我应该如何解决这个问题？我需要以某种方式将问题转化为回归吗？在这种情况下，如果数据中有三个标签（雨、干燥、多云），将百分比信息转换为连续值的正确方法是什么？ 感谢您抽出时间
In normal 2 or multi class classification problem, we can use any famous machine learning algorithm like Naive Bayes or SVM to train and test the model.
My problem is that I have been given weather data where the label variable is in the format of "20 % rain, 80 % dry" or "30% cloudy, 70% rain" etc.
How should I approach this problem? Will I need to covert the problem into regression somehow? In that case, if there are three labels (rain, dry, cloudy) in data, what may be the right approach to convert percentage information to continuous values?
Thanks for your time
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假设表达式“20% 雨，80% 干燥”和“30% 多云，70% 雨”表示概率，则这些类别是互斥的，并且我们可以忽略可能的序数关系（例如“干燥 > 多云 >其中，诸如多分类逻辑回归之类的模型可能适合这些值，就好像它们被分组或复制一样。
我想也可以采用其他临时程序，这可以最大限度地减少库尔贝克-莱布勒分歧等问题。
Assuming that the expressions "20 % rain, 80 % dry" and "30% cloudy, 70% rain" represent probabilities, that the classes are mutually exclusive and that we may ignore a possible ordinal relationship (such as "dry > cloudy > rain") among them, models such as polychotomous logistic regression may be fit to these values, as though they were grouped or replicated.
I suppose other, ad hoc procedures could be employed as well, which would minimize, for example, the Kullback–Leibler divergence.
我会推荐一个具有三个输出标签雨、干、云的神经网络。
如果您的数据带有标签“20% rain”，则实例的权重将为 0.2。如果没有“雨”标签，则应包含“假”。其他方法是使用相同转换约定的 3 个不同的回归分类器。我认为回归会效果更好。
神经网络将是不错的选择，因为它可以同时进行所有三个回归/分类，并且它们可以相互影响。此外，训练算法很简单。
I would recommend a neural network with three outputs labels Rain, Dry, Cloud.
If you have data with label "20 % rain" then weight of instance will be 0.2. If the are no "rain" label should contain "false". Other approach is to 3 different regression classifier with same converting convention. I think regression would work better.
Neural networks will be good choice because it can do all three regression/classification at once and they can influence on each other. Additionally the training algorithm is straightforward.