blocks|key|1089604|text|尝试多类逻辑回归-也可以看看这个https://gallery.cortanaanalytics.com/Experiment/da44bcd5dc2d4e059ebbaf94527d3d5b?fromlegacydomain=1|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1089605|entityMap|0|LINK|mutability|MUTABLE|url|https://gallery.cortanaanalytics.com/Experiment/da44bcd5dc2d4e059ebbaf94527d3d5b?fromlegacydomain=1^0|G|2R|0|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@$A|M|B|N|1|O]]|C|$]]|$1|D|3|-4|5|6|7|P|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]]]

Try a multi-class logistic regression - also look at this <a href="https://gallery.cortanaanalytics.com/Experiment/da44bcd5dc2d4e059ebbaf94527d3d5b?fromlegacydomain=1" rel="noreferrer">https://gallery.cortanaanalytics.com/Experiment/da44bcd5dc2d4e059ebbaf94527d3d5b?fromlegacydomain=1</a>

blocks|key|987709|text|除了逻辑回归，正如@neerajkh建议的那样，我也会尝试One+vs+All+classifiers。这种方法可以很好地处理多类问题(我假设你有许多输入，这是学生的分数)和许多输出(不同的学院)。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|987710|为了实现一对一的算法，我会使用Support+Vector+Machines+(支持向量机)。它是最强大的算法之一(直到深度学习出现，但这里不需要深度学习)|987711|如果您可以考虑更改框架，我建议使用python库。在python中，计算你所面临的问题非常简单，非常快。|987712|entityMap|0|LINK|mutability|MUTABLE|url|https://msdn.microsoft.com/en-us/library/azure/dn905887.aspx|1|https://msdn.microsoft.com/en-us/library/azure/dn913103.aspx|2|http://scikit-learn.org/stable/modules/svm.html^0|T|M|0|0|F|N|1|0|Q|6|2|0^^$0|@$1|2|3|4|5|6|7|T|8|@]|9|@$A|U|B|V|1|W]]|C|$]]|$1|D|3|E|5|6|7|X|8|@]|9|@$A|Y|B|Z|1|10]]|C|$]]|$1|F|3|G|5|6|7|11|8|@]|9|@$A|12|B|13|1|14]]|C|$]]|$1|H|3|-4|5|6|7|15|8|@]|9|@]|C|$]]]|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]]]]

Apart from logistic regression, as @neerajkh suggested, I would try as well
<a href="https://msdn.microsoft.com/en-us/library/azure/dn905887.aspx" rel="nofollow">One vs All classifiers</a>. This method use to work very well in multiclass problems (I assume you have many inputs, which are the marks of the students) and many outputs (the different colleges). 

To implement one vs all algorithm I would use <a href="https://msdn.microsoft.com/en-us/library/azure/dn913103.aspx" rel="nofollow">Support Vector Machines</a> (SVM). It is one of the most powerful algorithms (until deep learning came into the scene, but you don't need deep learning here)

If you could consider changing framework, I would suggest to use python libraries. In <a href="http://scikit-learn.org/stable/modules/svm.html" rel="nofollow">python</a> it is very straightforward to compute very very fast the problem you are facing.

blocks|key|4771862|text|使用随机森林树，并将此ML算法提供给OneVsRestClassifer，这是一个多类分类器|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4771863|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|$]]

use randomforesttrees and feed this ML algorithm to OneVsRestClassifer which is a multi class classifier

blocks|key|987747|text|为了与其他发帖者关于使用多类分类的建议保持一致，您可以使用人工神经网络(ANN)/multilayer感知器来做到这一点。每个输出节点可以是一所大学，因为您将使用sigmoid传递函数(logistic)，所以每个节点的输出可以直接视为该大学接收特定学生的概率(当尝试进行预测时)。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|987748|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|$]]

Keeping in line with other posters' suggestions of using multi-class classification, you could use artificial neural networks (ANNs)/multilayer perceptron to do this. Each output node could be a college and, because you would be using a sigmoid transfer function (logistic) the output for each of the nodes could be directly viewed as the probability of that college accepting a particular student (when trying to make predictions).

blocks|key|4771781|text|你为什么不试试softmax回归呢？|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4771782|在极其简单的术语中，Softmax接受一个输入，并生成属于每个类的输入的概率分布。因此，换句话说，基于一些输入(在这种情况下是年级)，您的模型可以输出概率分布，该概率分布表示给定的学生必须被每所大学录取的“机会”。|4771783|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|F|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|G|8|@]|9|@]|A|$]]|$1|D|3|-4|5|6|7|H|8|@]|9|@]|A|$]]]|E|$]]

Why don't you try softmax regression? 

In extremely simple terms, Softmax takes an input and produces the probability distribution of the input belonging to each one of your classes. So in other words based on some input (grade in this case), your model can output the probability distribution that represents the "chance" a given sudent has to be accepted to each college.

blocks|key|987869|text|我知道这是一个老帖子，但我也会继续添加我的2美分。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|987870|我建议添加多类、多标签分类器。这使您可以为一个学生找到多个大学。当然，对于ANN来说，这要容易得多，但配置起来要困难得多(比如网络的配置；节点/隐藏节点的数量，甚至是激活函数)。|987871|按照@Hoap+Humanoid的建议，最简单的方法是使用支持向量分类器。|987872|要做这些方法中的任何一种，你必须有一个很好的多样化的数据集。我不能说你需要试验的数据点的数量，但模型的准确性取决于数据点的数量及其多样性。|987873|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|J|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|K|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|L|8|@]|9|@]|A|$]]|$1|F|3|G|5|6|7|M|8|@]|9|@]|A|$]]|$1|H|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|I|$]]

I know this is an old thread but I will go ahead and add my 2 cents too.

I would recommend adding multi-class, multi-label classifier. This allows you to find more than one college for a student. Of course this is much easier to do with an ANN but is much harder to configure (say with the configuration of the network; number of nodes/hidden nodes or even the activation function for that matter).

The easiest method to do this as @Hoap Humanoid suggests is to use a Support Vector Classifier.

To do any of these method its a given that you have to havea well diverse data set. I cant say the number of data points you need that you have to experiment with but the accuracy of the model is dependent on number of data points and its diversity.

blocks|key|1089683|text|这是非常主观的。仅仅应用任何分类算法都不是一个好主意。如果不执行探索性数据分析并检查以下内容，除了缺失值之外，您就不能确保进行预测分析：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1089684|1089685|blockquote|1089686|1089687|对于异常值，变量与响应(学院)变量的双变量和多变量的定量和定性变量关系(+multivariate+variable.|1089688|Univariate，distribution.|unordered-list-item|1089689|
|1089690|Variable+variable.|1089691|1089692|Looking+|1089693|1089694|outliers)(多变量和多变量变量transformation.|1089695|1089696|Can是被分解成块的Y变量，例如位置，例如，考生是否可以是加州或纽约学院的一部分。如果加州的机会更高，那么上哪所大学呢？通过这种方式，您可以捕获线性%2B非线性relationships.|1089697|1089698|对于基础学习者，你可以拟合Softmax回归模型或1vsall+Logistic回归模型，这并不是很重要，非线性关系的购物车。我还会做K-nn和K-means来检查数据中的不同组，并决定预测性学习者。|1089699|我希望这是有意义的！|1089700|entityMap^0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|14|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|15|8|@]|9|@]|A|$]]|$1|C|3|-4|5|D|7|16|8|@]|9|@]|A|$]]|$1|E|3|-4|5|6|7|17|8|@]|9|@]|A|$]]|$1|F|3|G|5|6|7|18|8|@]|9|@]|A|$]]|$1|H|3|I|5|J|7|19|8|@]|9|@]|A|$]]|$1|K|3|L|5|6|7|1A|8|@]|9|@]|A|$]]|$1|M|3|N|5|J|7|1B|8|@]|9|@]|A|$]]|$1|O|3|L|5|6|7|1C|8|@]|9|@]|A|$]]|$1|P|3|Q|5|J|7|1D|8|@]|9|@]|A|$]]|$1|R|3|L|5|6|7|1E|8|@]|9|@]|A|$]]|$1|S|3|T|5|J|7|1F|8|@]|9|@]|A|$]]|$1|U|3|L|5|6|7|1G|8|@]|9|@]|A|$]]|$1|V|3|W|5|J|7|1H|8|@]|9|@]|A|$]]|$1|X|3|-4|5|6|7|1I|8|@]|9|@]|A|$]]|$1|Y|3|Z|5|6|7|1J|8|@]|9|@]|A|$]]|$1|10|3|11|5|6|7|1K|8|@]|9|@]|A|$]]|$1|12|3|-4|5|6|7|1L|8|@]|9|@]|A|$]]]|13|$]]

This is very subjective. Just applying any algorithm that classifies into categories won't be a good idea. Without performing Exploratory Data Analysis and checking following things you can't be sure of a doing predictive analytics, apart from missing values:

<blockquote>
 <ol>
 <li>Quantitative and Qualitative variable.</li>
 <li>Univariate, Bivariate and multivariate distribution.</li>
 <li>Variable relationship to your response(college) variable.</li>
 <li>Looking for outliers(multivariate and univariate).</li>
 <li>Required variable transformation.</li>
 <li>Can be the Y variable broken down into chunks for example location, for example whether a candidate can be a part of Colleges in California or New York. If there is a higher chance of California, then what college. In this way you could capture Linear + non-linear relationships.</li>
 </ol>
</blockquote>

For base learners you can fit Softmax regression model or 1 vs all Logistic regression which does not really matters a lot and CART for non-linear relationship. I would also do K-nn and K-means to check for different groups within data and decide on predictive learners. 

I hope this makes sense!

blocks|key|1095721|text|最小二乘支持向量机(LSSVM)是一种强大的支持向量机算法。有关更多信息，请访问http://www.esat.kuleuven.be/sista/lssvmlab/。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1095722|entityMap|0|LINK|mutability|MUTABLE|url|http://www.esat.kuleuven.be/sista/lssvmlab/^0|14|17|0|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@$A|M|B|N|1|O]]|C|$]]|$1|D|3|-4|5|6|7|P|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]]]

The Least-square support vector machine (LSSVM) is a powerful algorithm for this application. Visit <a href="http://www.esat.kuleuven.be/sista/lssvmlab/" rel="nofollow noreferrer">http://www.esat.kuleuven.be/sista/lssvmlab/</a> for more information.

I have data of a lot of students who got selected by some colleges based on their marks. Iam new to machine Learning. Can I have some suggestions how can I add Azure Machine Learning for predicting the colleges that they can get based on their marks

Machine Learning Suggestions

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。

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我有很多学生的数据，他们是根据自己的成绩被一些大学选中的。我是机器学习的新手。我可以有一些建议吗?我如何添加Azure机器学习来根据他们的分数预测他们可以获得的大学

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