<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>

CC BY-SA 4.0
2019-12-09T12:57:26.873Z
why don't you just use 4 classes in your random forest classifier?
-high positive
-normal positive
-extra high positive
-negative
Lustwelpintje
CC BY-SA 4.0
2019-12-09T16:08:50.603Z
@Lustwelpintje Is  random forest the classifier that could obtain the most success cases ?
notarealgreal
CC BY-SA 4.0
2019-12-10T13:15:54.037Z
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.
Lustwelpintje
CC BY-SA 4.0
2019-12-13T17:32:54.197Z
@Lustwelpintje what about Pytorch, could be a good option ?
notarealgreal
CC BY-SA 4.0
2019-12-16T09:12:48.210Z
Although I have not used it yet, From what I heard Pytorch will also be fine.
Lustwelpintje
<machine-learning><classification><decision-trees><random-forests><k-means>
How can I classify instances into two categories and then into sub-categories, when the number of features is high?
notarealgreal
# 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**: @username_1 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**: @username_1 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.  

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## 0 answers

