blocks|key|4022109|text|我会研究运筹学，因为它通常是一个最小化或最大化的问题。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4022110|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

I would look into operation research because usually it's a minimize or maximize problem.

blocks|key|3539571|text|因为您有一个分类列，它是优先级列，所以您可以尝试使用决策树。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3539572|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

As you have a classification column which is the priority column, you can try for decision tree.

blocks|key|5750055|text|您已经说明了一个回归问题，因为您正在尝试预测一个连续的数值。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|5750056|对于每个数据实例，您可以提取特征并关联一个值(1-5)。特征可以是在用餐期间存在一个菜(例如has_cheesecake，has_orange_juice)，其中每个特征都是一个布尔值。假设有N个可能的菜肴；那么每顿饭都是一个数据实例(也称为特征向量)，具有N个特征和一个关联值。下面是一个使用N=12的示例，其中最后(第13)列是值。|5750057|0+0+0+1+1+0+0+0+1+0+0+0+5
1+0+0+1+0+1+0+0+0+0+0+0+3
0+0+0+0+0+0+1+1+0+0+0+1+4|code-block|syntax|javascript|5750058|然后，您可以将其输入到像Weka这样的机器学习程序中，它将为您创建一个回归模型。然后，当您想要预测用户对新餐点的排名时，您可以输入一个新的向量，其中最后一列是未知的，如下所示：|5750059|0+0+1+0+0+0+1+0+0+0+0+1+?|5750060|软件会给你返回值，比如3.9。|5750061|entityMap^0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|R|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|S|8|@]|9|@]|A|$G|H]]|$1|I|3|J|5|6|7|T|8|@]|9|@]|A|$]]|$1|K|3|L|5|F|7|U|8|@]|9|@]|A|$G|H]]|$1|M|3|N|5|6|7|V|8|@]|9|@]|A|$]]|$1|O|3|-4|5|6|7|W|8|@]|9|@]|A|$]]]|P|$]]

You have stated a regression problem because you are trying to predict a continuous numerical value.

For each data instance, you can extract features and associate a value (1-5). The features can be the existence of a dish during the meal (e.g. has_cheesecake, has_orange_juice), where each feature is a boolean. Suppose there are N possible dishes; then each meal is a data instance (also known as a feature vector) with N features and an associated value. Below is an example with N=12, where the last (13th) column is the value.

<pre><code>0 0 0 1 1 0 0 0 1 0 0 0 5
1 0 0 1 0 1 0 0 0 0 0 0 3
0 0 0 0 0 0 1 1 0 0 0 1 4
</code></pre>

You can then feed this into a machine learning program like Weka, and it will create a regression model for you. Then when you want to predict the user's ranking for a new meal, you feed in a new vector where the last column is unknown, like the following:

<pre><code>0 0 1 0 0 0 1 0 0 0 0 1 ?
</code></pre>

The software will return a value to you, like 3.9.

blocks|key|191934|text|你问的基本上是sentiment+detection，它已经变得非常流行，用于预测用户对产品的态度。这里有一个seminal+paper，这取决于你在学术上的倾向。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|191935|你可以把这看作是一个回归问题，但很多时候人们忽略了类之间存在顺序关系的事实。如果你对这顿饭中的食物除了它们的名字之外没有更多的信息，我不确定我不能指望你在这方面做得很好。如果可能的话，你应该寻找课程的特征表示，以提高你预测价值的能力。|191936|entityMap|0|LINK|mutability|MUTABLE|url|http://en.wikipedia.org/wiki/Sentiment_analysis|1|http://www.cs.cornell.edu/home/llee/papers/pang-lee-stars.home.html^0|7|J|0|1J|D|1|0|0^^$0|@$1|2|3|4|5|6|7|P|8|@]|9|@$A|Q|B|R|1|S]|$A|T|B|U|1|V]]|C|$]]|$1|D|3|E|5|6|7|W|8|@]|9|@]|C|$]]|$1|F|3|-4|5|6|7|X|8|@]|9|@]|C|$]]]|G|$H|$5|I|J|K|C|$L|M]]|N|$5|I|J|K|C|$L|O]]]]

What you're asking is basically <a href="http://en.wikipedia.org/wiki/Sentiment_analysis" rel="nofollow">sentiment detection</a>, which has become very popular for doing things like predicting a user's attitude towards a product. Here's a <a href="http://www.cs.cornell.edu/home/llee/papers/pang-lee-stars.home.html" rel="nofollow">seminal paper</a>, depending on how academically inclined you are.

You could look at this as a regression problem, but a lot of the time people ignore the fact that there are ordinal relations between the classes. If you have no more information on the items in the meal than their names, I'm not sure I'd expect you to do very well at it. You should look for a feature-representation of the courses if at all possible, to improve your ability to predict values.

blocks|key|191960|text|为了选择最好的分类器，您应该估计问题的几个额外特征，例如属性值的近似数量(即，有多少主菜、甜点和饮料可供选择)，以及在进行预测之前您希望提供多少训练示例。并不是所有的分类器都擅长处理稀疏数据。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|191961|如果您有大量的训练示例(相对于属性值的数量)，则decision+tree+classifier是一个很好的起点。决策树的好处之一是，学习树的结构是直观的，并提供了对重要属性(以及属性的组合)的简单解释。|offset|length|191962|entityMap|0|LINK|mutability|MUTABLE|url|http://en.wikipedia.org/wiki/Decision_tree_learning^0|0|O|O|0|0^^$0|@$1|2|3|4|5|6|7|N|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|O|8|@]|9|@$D|P|E|Q|1|R]]|A|$]]|$1|F|3|-4|5|6|7|S|8|@]|9|@]|A|$]]]|G|$H|$5|I|J|K|A|$L|M]]]]

To pick the best classifier, there are several additional characteristics of your problem you should estimate, such as the approximate number of attribute values (i.e., how many entrees, desserts, and drinks are there from which to choose) and approximately how many training examples would you want to provide prior to making predictions. Not all classifiers are good at dealing with sparse data.

If you have a large number of training examples (relative to the number of attribute values), a <a href="http://en.wikipedia.org/wiki/Decision_tree_learning" rel="nofollow">decision tree classifier</a> is a good place to start. One of the benefits of a decision tree is that the structure of the learned tree is intuitive and provides a simple interpretation of what are the important attributes (and combinations of attributes).

blocks|key|3539759|text|有许多算法可以解决你的问题，其中一些可以是decision+trees，neural+nets或support+vector+machines。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|3539760|然而，当你使用用户意见时，在某些情况下，你可能无法在所有产品中获得用户意见(他们可以跳过)。我不知道这是不是你的情况，也许你强迫他们对所有的产品进行排名。但是，如果您为用户提供跳过的选项，您将最终得到一些未标记的产品。在这种情况下，您可以使用协作过滤。这种方法即使在我解释的情况下也能预测新用户的估计选择。|3539761|你可以在Ng's+course上找到一个很好的教程。|3539762|entityMap|0|LINK|mutability|MUTABLE|url|http://scikit-learn.org/stable/modules/tree.html|1|http://scikit-learn.org/dev/modules/neural_networks_supervised.html|2|http://scikit-learn.org/stable/modules/svm.html|3|http://www.ml-class.org/course/video/preview_list^0|L|E|0|10|B|1|1C|N|2|0|0|4|B|3|0^^$0|@$1|2|3|4|5|6|7|V|8|@]|9|@$A|W|B|X|1|Y]|$A|Z|B|10|1|11]|$A|12|B|13|1|14]]|C|$]]|$1|D|3|E|5|6|7|15|8|@]|9|@]|C|$]]|$1|F|3|G|5|6|7|16|8|@]|9|@$A|17|B|18|1|19]]|C|$]]|$1|H|3|-4|5|6|7|1A|8|@]|9|@]|C|$]]]|I|$J|$5|K|L|M|C|$N|O]]|P|$5|K|L|M|C|$N|Q]]|R|$5|K|L|M|C|$N|S]]|T|$5|K|L|M|C|$N|U]]]]

There are many algorithms that can fit in your problem, some of them can be <a href="http://scikit-learn.org/stable/modules/tree.html" rel="nofollow">decision trees</a>, <a href="http://scikit-learn.org/dev/modules/neural_networks_supervised.html" rel="nofollow">neural nets</a> or <a href="http://scikit-learn.org/stable/modules/svm.html" rel="nofollow">support vector machines</a>. 

However, when you are working with user opinions, maybe in some cases you don't get the user opinion in all your products (they can skip). I don't know if this is your case, maybe you force them to rank all the products. However, if you give the user the option to skip, you will end up with some of your products that haven't been labelled. In that situation you could use collaborative filtering. This method predicts the estimated selection of a new user even in the situation I explained.

You can find a good tutorial in <a href="http://www.ml-class.org/course/video/preview_list" rel="nofollow">Ng's course</a>.

blocks|key|192032|text|如果您希望根据用户的相似性(相同的用户、相同的食物)创建一个模型，那么可以使用协作过滤创建一个模型。(面向每个用户的载体)+keras、scikit+learn和pandas等库非常有用和简单。如果您想编写自己的模型，那么矩阵分解可能会有所帮助。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|192033|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

If you would like to create a model from the similarities of the users(same user same food) then create a model with collaborative filtering. (Vectors For every user ) Libraries such as keras, scikit learn and pandas are very useful and easy. 
If you would like to write your own model then Matrix factorization may be helpful.

I've seen some machine learning questions on here so I figured I would post a related question:
Suppose I have a randomly generated food list which includes an entree, dessert, and a drink. An example would be Chicken, cheesecake, orange juice. The user would rank how they personally like this combination from a scale of 1-5. After reach rating, another random set of food would appear. 

My question is, what machine learning technique/algorithm would I use to predict what the user would rank a randomly generated set from all their previous data? In essence, use their opinion to predict their rating of new food sets. Any sites/books that may help?

Application of machine learning

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我在这里看到了一些机器学习的问题，所以我想我应该发布一个相关的问题:假设我有一个随机生成的食物列表，其中包括主菜、甜点和饮料。例如鸡肉、芝士蛋糕、橙汁。用户会从1-5的范围内对他们个人喜欢这个组合的程度进行排名。在reach分级之后，将出现另一组随机的食物。我的问题是，我将使用什么机器学习技术/算法来预测用户将根据其所...

问机器学习的应用
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