Body: <p>I'm working with a problem where I have a lot of variables for different cases of different users. Depending on the values of the different variables of a concrete user in a concrete case, the algorithm must classify that user in that case as:</p>
  
  <ul>
  <li>Positive</li>
  <li>Negative</li>
  </ul>
  
  <p>But if the user is classified as positive, it must be classified as:</p>
  
  <ul>
  <li>Positive normal</li>
  <li>Positive high</li>
  <li>Positive extra-high</li>
  </ul>
  
  <p>If a case is positive, depending on the values of a part of the parameters, we know that the probability to be, for example, <em>positive normal</em> is bigger or lower.</p>
  
  <p>To sum up, I see the problem as a spam detector with different spam types.</p>
  
  <p>May this work if I apply an algorithm like:</p>
  
  <ul>
  <li>Random Forest</li>
  <li>Decision Tree</li>
  </ul>
  
  <p>Or maybe I can include the negative case as a new group and then implement a K-means algorithm? Maybe this would help to find new groups of parameters that will say that the concrete case forms part of a group for sure.</p>
  
  <p>Which one will fit best with a lot of parameters?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-12-09T12:57:26.873Z
Comments.Text: why don't you just use 4 classes in your random forest classifier?
  -high positive
  -normal positive
  -extra high positive
  -negative
Comments.UserDisplayName: Lustwelpintje
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-12-09T16:08:50.603Z
Comments.Text: @Lustwelpintje Is  random forest the classifier that could obtain the most success cases ?
Comments.UserDisplayName: notarealgreal
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-12-10T13:15:54.037Z
Comments.Text: Depending on the data and complexity.. I have had some really nice results with a simple random forest. I do believe a neural network can classify more complex functions better than a random forest, but it takes more time to get it right.
Comments.UserDisplayName: Lustwelpintje
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-12-13T17:32:54.197Z
Comments.Text: @Lustwelpintje what about Pytorch, could be a good option ?
Comments.UserDisplayName: notarealgreal
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-12-16T09:12:48.210Z
Comments.Text: Although I have not used it yet, From what I heard Pytorch will also be fine.
Comments.UserDisplayName: Lustwelpintje
Tags: <machine-learning><classification><decision-trees><random-forests><k-means>
Title: How can I classify instances into two categories and then into sub-categories, when the number of features is high?
UserDisplayName: notarealgreal
ThreadText: # How can I classify instances into two categories and then into sub-categories, when the number of features is high?
  
  (Asked by: username_0 on 2019-12-06)
  
  I'm working with a problem where I have a lot of variables for different cases of different users. Depending on the values of the different variables of a concrete user in a concrete case, the algorithm must classify that user in that case as:
  
  * Positive
  * Negative
  
  But if the user is classified as positive, it must be classified as:
  
  * Positive normal
  * Positive high
  * Positive extra-high
  
  If a case is positive, depending on the values of a part of the parameters, we know that the probability to be, for example, *positive normal* is bigger or lower.
  
  To sum up, I see the problem as a spam detector with different spam types.
  
  May this work if I apply an algorithm like:
  
  * Random Forest
  * Decision Tree
  
  Or maybe I can include the negative case as a new group and then implement a K-means algorithm? Maybe this would help to find new groups of parameters that will say that the concrete case forms part of a group for sure.
  
  Which one will fit best with a lot of parameters?
  
  **Tags:** machine-learning, classification, decision-trees, random-forests, k-means
  > **Comments:**
  > **username_1**: why don't you just use 4 classes in your random forest classifier? -high positive -normal positive -extra high positive -negative  
  > **username_0**: @Lustwelpintje Is  random forest the classifier that could obtain the most success cases ?  
  > **username_1**: Depending on the data and complexity.. I have had some really nice results with a simple random forest. I do believe a neural network can classify more complex functions better than a random forest, but it takes more time to get it right.  
  > **username_0**: @Lustwelpintje what about Pytorch, could be a good option ?  
  > **username_1**: Although I have not used it yet, From what I heard Pytorch will also be fine.  
  
  ---
  
  ## 0 answers
  
  