How to build a confusion matrix for a multiclass classifier? I have a problem with 6 classes. So I build a multiclass classifier, as follows: for each class, I have one Logistic Regression classifier, using One vs. All, which means that I have 6 different classifiers.
I can report a confusion matrix for each one of my classifiers. But, I would like to report a confusion matrix for ALL the classifiers, as I've seen in a  lot of examples here.
How can I do it? Do I have to change my classification strategy, using a One vs. One algorithm instead of One vs. All? Because on these confusion matrices, the reports says the false positives for each class.
Example of a multiclass confusion matrix

I would like to find the number of misclassified items. In the first row, there are 137 examples of class 1 that were classified as class 1, and 13 examples of class 1 that were classified as class 2. How to get this number?
 A: Presumably, you are using these classifiers to help choose one particular class for a given set of feature values (as you said you are creating a multiclass classifier). 
So, lets say you have $N$ classes, then your confusion matrix would be an $N\times N$ matrix, with the left axis showing the true class (as known in the test set) and the top axis showing the class assigned to an item with that true class. Each element $i,j$ of the matrix would be the number of items with true class $i$ that were classified as being in class $j$.
This is just a straightforward extension of the 2-class confusion matrix.
A: Using the matrix attached in the question and considering the values in the vertical axis as the actual class, and the values in the horizontal axis the prediction. Then for the Class 1:

*

*True Positive = 137 -> samples of class 1, classified as class 1

*False Positive = 6 -> (1+2+3) samples of classes 2, 3 and 4, but
classified as class 1

*False Negative = 18 -> (13+3+1+1) samples of class 1, but classified
as classes 2, 3, 6 and 7

*Ture Negative = 581 -> (55+1+6...+2+26) The sum of all the values in the matrix except those in column 1 and row 1

A: While there are some answers already on this forum I thought I'd give the explicit equations to make it more definite:
Assuming you have a multi-class confusion matrix of the form,
\begin{align}
C=\text{Actual}\begin{matrix}
& \text{Classifed} & \\
c_{11} & ... & c_{1n}\\ 
\vdots  & \ddots  & \\ 
c_{n1} &  & c_{nn}
\end{matrix}
\end{align}
The confusion elements for each class are given by:
$tp_i = c_{ii}$
$fp_i = \sum_{l=1}^n c_{li} - tp_i$
$fn_i = \sum_{l=1}^n c_{il} - tp_i$
$tn_i = \sum_{l=1}^n \sum_{k=1}^n c_{lk} - tp_i - fp_i - fn_i$
