Confusion matrix neural network

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John
John on 6 May 2013
Commented: Vikas on 10 Mar 2016
Hello,
The confusion matrix for my NN for classification is below. I'm struggling to understand the numbers. I know the overall correctly classified data is 81.5%.
I would be grateful if somebody had the time to answer a couple of questions.
If I take the 1st row as an example.
What does 22.2% represent?
What does 4 and 14.8% represent?
What does 60.0% (in green) represent?
What does the bottom grey row represent for example 85.7%?
Thanks for your help

Accepted Answer

Greg Heath
Greg Heath on 7 May 2013
Look at column one for class 1 targets
There were 7 class 1 targets
6 were assigned correctly(GREEN) to output class 1
1 was assigned incorrectly (RED) to output class 2
0 were assigned to output class 3 (Ignore colors for 0 entries)
100*6/7 = 85.7% (GREEN)of class 1 targets were correctly assigned
100*1/7 = 14.3% (RED) of class 1 targets were incorrectly assigned
Look at row two for targets assigned to class 2
17 targets were assigned to output class 2
1 target from class 1 was incorrectly(RED) assigned to class 2
16 targets from class 2 were correctly(GREEN) assigned to class2
100*16/17 = 94.1%(GREEN) of assignments to class 2 were correct
100*1/17 = 5.9%(RED) of assignments to class 2 were incorrect
Look at interior square percentages
There were 6+1+16+4 = 27 targets
100*6/27 = 22.2%
100*1/27 = 3.7%
100*16/27 = 59.3%
100*4/27 = 14.8%
If it makes you feel any better, I do not like the format (e.g., I used to use the rows for target classes). However, using the column target format, I use a count confusion matrix and a percent confusion matrix:
6 0 4 10
1 16 0 17
7 16 4 27
and
85.7 0 100 37
14.3 100 0 63
100 100 100 100
Hope this helps.
Thank you for formally accepting my answer
Greg

More Answers (1)

D C
D C on 21 Oct 2013
Edited: D C on 21 Oct 2013
Is there any way to display this values in workspace? I got 10 classes and I cannot read them from the diagram because they overlap.
  1 Comment
Vikas
Vikas on 10 Mar 2016
[c,cm,ind,per] = confusion(targets,outputs)

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