Answers.Body: <p>The term you need  is “model ensembles”, that’s the way models are combined.  Pretty hard to be more specific since you don’t give a language or any other details.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-01-01T23:10:52.953Z
Answers.Text: Sorry for that, I just know to little about it to be more specific about what I'm after so looking for guidance, will update the post with more info, thank you for your patience.
Answers.UserDisplayName: MisterButter
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-01-01T23:08:12.937Z
Answers.LastActivityDate: 2019-01-01T23:08:12.937Z
Answers.UserDisplayName: Sol7
Body: <p>I want to create a NHL game predictor and have already trained one neural network on game data. </p>
  
  <p>What I would like to do is train another model on player seasonal/game data and combine the two models to archive better accuracy. </p>
  
  <p>Is this approach feasible? If it is, how do I go about doing it?</p>
  
  <p>EDIT:</p>
  
  <p>I have currently trained a neural network to classify the probability of the home team winning a game on a dataset that looks like this:</p>
  
  <pre><code>h_Won/Lost  h_metric2 h_metric3 h_metric4 a_metric2 a_metric3 a_metric4 h_team1 h_team2 h_team3 h_team4 a_team1 a_team2 a_team3 a_team4
   1            10       10         10        10         10        10      1       0        0      0         0      1        0      0
   1            10       10         10        10         10        10      1       0        0      0         0      1        0      0
   1            10       10         10        10         10        10      1       0        0      0         0      1        0      0
  </code></pre>
  
  <p>and so on.</p>
  
  <p>I am preparing a dataset of player-data for each game that will have the shape of this:</p>
  
  <pre><code>Player     PlayerID    Won/Lost     team      opponent     metric1     metric2   
   Henke         1           1          NY          CAP         10          10
  </code></pre>
  
  <p>Hopefully, this new dataset will have some accuracy on if team is going to have some predictive features that are good and recognised. </p>
  
  <p>Now, say I have these two trained Nural Networks and they both have an accuracy of 70% by them self. But I want to combine them both in the hopes to achieve better predictability. How is this archived? How will the test-dataset be structured?</p>
  
Tags: <neural-networks><deep-learning><data-science>
Title: is it possible to train several Neural Networks on different types of data and combine them?
UserDisplayName: MisterButter
ThreadText: # is it possible to train several Neural Networks on different types of data and combine them?
  
  (Asked by: username_0 on 2019-01-01)
  
  I want to create a NHL game predictor and have already trained one neural network on game data. 
  What I would like to do is train another model on player seasonal/game data and combine the two models to archive better accuracy. 
  Is this approach feasible? If it is, how do I go about doing it?
  EDIT:
  I have currently trained a neural network to classify the probability of the home team winning a game on a dataset that looks like this:
  h_Won/Lost  h_metric2 h_metric3 h_metric4 a_metric2 a_metric3 a_metric4 h_team1 h_team2 h_team3 h_team4 a_team1 a_team2 a_team3 a_team4
   1            10       10         10        10         10        10      1       0        0      0         0      1        0      0
   1            10       10         10        10         10        10      1       0        0      0         0      1        0      0
   1            10       10         10        10         10        10      1       0        0      0         0      1        0      0
  
  and so on.
  I am preparing a dataset of player-data for each game that will have the shape of this:
  Player     PlayerID    Won/Lost     team      opponent     metric1     metric2   
   Henke         1           1          NY          CAP         10          10
  
  Hopefully, this new dataset will have some accuracy on if team is going to have some predictive features that are good and recognised. 
  Now, say I have these two trained Nural Networks and they both have an accuracy of 70% by them self. But I want to combine them both in the hopes to achieve better predictability. How is this archived? How will the test-dataset be structured?
  
  **Tags:** neural-networks, deep-learning, data-science
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2019-01-01. Score: 1
  
  The term you need is “model ensembles”, that’s the way models are combined. Pretty hard to be more specific since you don’t give a language or any other details.
  
  > **Comments:**
  > **username_0**: Sorry for that, I just know to little about it to be more specific about what I'm after so looking for guidance, will update the post with more info, thank you for your patience.  
  
  ---
  
  