Confusion matrix / Matching matrix

CFMATRIX calculates the confusion matrix for any classification algorithm that generates a list of c

Description

function [confmatrix] = cfmatrix(actual, predict, classlist, per)

% Outputs: confusion matrix
%
%
% Inputs:
% 1. actual / 2. predict
% The inputs provided are the 'actual' classes vector and the 'predict'ed classes vector. The actual classes are the classes to which the input feature vectors belong. The predicted classes are the class to which the input feature vectors are predicted to belong to, based on a prediction algorithm.
% The length of actual class vector and the predicted class vector need to be the same. If they are not the same, an error message is displayed.
% 3. classlist
% The third input provides the list of all the classes {p,n,...} for which the classification is being done. All classes are numbers.
% 4. per = 1/0 (default = 0)
% This parameter when set to 1 provides the values in the confusion matrix as percentages. The default provides the values in numbers.
%
% Example:
% >> a = [ 1 2 3 1 2 3 1 1 2 3 2 1 1 2 3];
% >> b = [ 1 2 3 1 2 3 1 1 1 2 2 1 2 1 3];
% >> Cf = cfmatrix(a, b);

• MATLAB release: MATLAB 7.0.1 (R14SP1)

[Avinash Uppuluri]

Hi Avinash,
 
I stumbled into your work about confusion matrix in matlab forum....My question, how do obtain true positives,true negative, false positive and false negative from your code?
 
Thanks
--
Fess Iyuke

Hi Fess,

Please add this piece of code at the end of the program you downloaded and it should give you the TP, FP, FN, TN values. Hope this helps. If there are any issues with the code please let me know. I will try to update my submission soon.

% True Postive [TP] = Condition Present + Positive result
% False Positive [FP] = Condition absent + Positive result [Type
% I error]
% False (invalid) Negative [FN] = Condition present + Negative result [Type
% II error]
% True (accurate) Negative [TN] = Condition absent + Negative result

disp('------------------------------------------');
disp(' Actual Classes');
disp(line_two);

temps = sprintf(' TP ');
for i = 1:n_class
    temps = strcat(temps,sprintf(' | %2.1f ',confmatrix(i,i)));
end
disp(temps);
clear temps

temps = sprintf(' FP ');
for i = 1:n_class
    temps = strcat(temps,sprintf(' | %2.1f ',sum(confmatrix(i,:))-confmatrix(i,i) ));
end
disp(temps);
clear temps

temps = sprintf(' FN ');
for i = 1:n_class
    temps = strcat(temps,sprintf(' | %2.1f ',sum(confmatrix(:,i))-confmatrix(i,i) ));
end
disp(temps);
clear temps

temps = sprintf(' TN ');
for i = 1:n_class
    temps = strcat(temps,sprintf(' | %2.1f ',sum(diag(confmatrix))-confmatrix(i,i) ));
end
disp(temps);
clear temps

[Atam Tsalikian]

Hi. How can we obtain accuracy and precision from this code?
Thanxs!

[Avinash Uppuluri]

Hi Atam,
If you can give me a definition of how you want to measure accuracy and precision I can include it in the code.

Thanks.

[Atam Tsalikian]

Take a look here: http://www2.cs.uregina.ca/~hamilton/courses/831/notes/confusion_matrix/confusion_matrix.html
I think it's helpfull.

[Madi]

Hi avinash, i think the formula for calculated TN (True Negative) is something wrong... I'm sorry if wrong...