title: How to label special cases in RandomForestRegressor in sklearn in python
body: <p>I have a set of numerical features (f1, f2, f3, f4, f5) as follows for each user in my dataset.</p>
  
  <pre><code>       f1   f2  f3  f4  f5
  user1  0.1  1.1  0 1.7  1
  user2  1.1  0.3  1 1.3  3
  user3  0.8  0.3  0 1.1  2
  user4  1.5  1.2  1 0.8  3
  user5  1.6  1.3  3 0.3  0
  </code></pre>
  
  <p>My target output is a prioritised user list. i.e. as shown in the example below.</p>
  
  <pre><code>       f1   f2  f3  f4  f5  target_priority
  user1  0.1  1.1  0 1.7  1       2
  user2  1.1  0.3  1 1.3  3       1
  user3  0.8  0.3  0 1.1  2       5
  user4  1.5  1.2  1 0.8  3       3
  user5  1.6  1.3  3 0.3  0       4
  </code></pre>
  
  <p>I want to use these features in a way that reflect the priority of the user. Currently, I am using <code>sklearns</code>RandomForestRegressor` to perform this task.</p>
  
  <p>However, I got my real dataset recently and it has some users with no priority label. That is because such users are not important to our company (more like general users). </p>
  
  <p>Example (How real dataset looks like):</p>
  
  <pre><code>       f1   f2  f3  f4  f5  target_priority
  user1  0.1  1.1  0 1.7  1       2
  user2  1.1  0.3  1 1.3  3       2
  user3  0.8  0.3  0 1.1  2       N/A
  user4  1.5  1.2  1 0.8  3       N/A
  user5  1.6  1.3  3 0.3  0       1
  </code></pre>
  
  <p>In such special cases (that does not have a <code>priority label</code>), is it good to give them a special symbol or a priority level that is much much lower than the existing priorities (e.g., <code>100000000000000000 priority</code>)? <strong>How does such special cases are handled in RandomForestRegressor?</strong></p>
  
  <p>I am happy to provide more details if needed?</p>