text: user4595: I have inferred a confusion matrix of training and test set by neural network. I want to know which members are in the confusion matrix.
  > table(test$TRG,predicted.nn.values$net.result)
  
      0 1
    0 5 4
    1 5 3
  > 
  
  > head(train[,1:4])
    TRG      ALB     AQP9   CALML5
  1   1 5.865827 8.190945 6.705303
  2   1 6.998435 8.424261 8.505591
  3   1 7.424512 8.716471 7.249556
  4   1 7.442049 8.325263 8.809286
  6   1 5.893411 8.199990 6.677618
  7   1 7.288030 9.143510 7.088598
  > 
  
  > head(test[,1:4])
     TRG      ALB     AQP9    CALML5
  5    1 6.369307 7.954310  6.920290
  8    1 6.181902 8.651442  7.225389
  10   1 6.119359 9.345270  6.829623
  13   1 7.775533 9.016272  7.976813
  14   1 5.913656 9.484457 10.609013
  18   1 6.603138 7.908560  8.827173
  > 
  
  How can I know which patients are false positive or true negative? For example I have 5 patients predicted correctly, how can I know the name of these patients?
  
  user3848: This does not look a great confusion matrix, but it is a very, very cool approach. The cool thing about machine learning is the ability to examine lots of models within confusion matrices.
  
  To answer your question, you want a heat map of the regression weights to establish who is who, i.e. which patient is false positive etc... This is the only way I'm aware of to analyse a neural network. This looks like R, I only know Scikit learn.
  
original_text: 4595: <p>I have inferred a confusion matrix of training and test set by neural network. I want to know which members are in the confusion matrix.</p>
  
  <pre><code>&gt; table(test<span class="math-container">$TRG,predicted.nn.values$</span>net.result)
  
      0 1
    0 5 4
    1 5 3
  &gt; 
  
  &gt; head(train[,1:4])
    TRG      ALB     AQP9   CALML5
  1   1 5.865827 8.190945 6.705303
  2   1 6.998435 8.424261 8.505591
  3   1 7.424512 8.716471 7.249556
  4   1 7.442049 8.325263 8.809286
  6   1 5.893411 8.199990 6.677618
  7   1 7.288030 9.143510 7.088598
  &gt; 
  
  &gt; head(test[,1:4])
     TRG      ALB     AQP9    CALML5
  5    1 6.369307 7.954310  6.920290
  8    1 6.181902 8.651442  7.225389
  10   1 6.119359 9.345270  6.829623
  13   1 7.775533 9.016272  7.976813
  14   1 5.913656 9.484457 10.609013
  18   1 6.603138 7.908560  8.827173
  &gt; 
  </code></pre>
  
  <p>How can I know which patients are false positive or true negative? For example I have 5 patients predicted correctly, how can I know the name of these patients?</p>
   3848: <p>This does not look a great confusion matrix, but it is a very, very cool approach. The cool thing about machine learning is the ability to examine lots of models within confusion matrices.</p>
  
  <hr>
  
  <p>To answer your question, you want a heat map of the regression weights to establish who is who, i.e. which patient is false positive etc... This is the only way I'm aware of to analyse a neural network. This looks like R, I only know Scikit learn.</p>
  