ashokpant / accuracy-evaluation-matlab

A MATLAB implementation for calculating the accuracy metrics (Accuracy, Error Rate, Precision(micro/macro), Recall(micro/macro), Fscore(micro/macro)) for classification tasks based on the paper http://www.sciencedirect.com/science/article/pii/S0306457309000259.

Accuracy evaluation

A MATLAB implementation for calculating accuracy metrics (Accuracy, Error Rate, Precision(micro/macro), Recall(micro/macro), Fscore(micro/macro)) for classification tasks based on the paper A systematic analysis of performance measures for classification tasks and MATLAB confusion implementation.

Uses

targets = [1, 1, 0, 0, 0, 0;
           0, 0, 1, 1, 0, 0;
           0, 0, 0, 0, 1, 1];
           
outputs = [0.1, 0.86, 0.2, 0.1, .02, 0.1;
           0.4, 0.12, 0.768, 0.145, 0.1, 0.8;
           0.454, 0.35, 0.21, 0.0, 0.89, 0.9999];

eval = evaluation(targets,outputs);

fprintf('%s\n','Confusion Results');
fprintf('\tConfusion value = %0.2f\n', eval.confusion.c);

fprintf('%s\n','Confusion Matrix');
for row = 1:size(eval.confusion.cm,1)
    fprintf('\t');
    for col = 1:size(eval.confusion.cm,2)
    fprintf('%0.2f ', eval.confusion.cm(row,col));
    end
    fprintf('\n');
end

fprintf('%s\n','Indices');
for row = 1:size(eval.confusion.ind,1)
    for col = 1:size(eval.confusion.ind,2)
    s = size(eval.confusion.ind{row,col},2);
    if s == 0
        fprintf('\t[]');
    elseif s == 1
        fprintf('\t[%d]',eval.confusion.ind{row,col}(1));
    else
        fprintf('\t[');
        for ind = 1 : s-1
            fprintf('%d,', eval.confusion.ind{row,col}(ind));
        end
        fprintf('%d] ',eval.confusion.ind{row,col}(s));
    end
    end
    fprintf('\n');
end

fprintf('%s\n','Percentages');
for row = 1:size(eval.confusion.per,1)
    fprintf('\t');
    for col = 1:size(eval.confusion.per,2)
    fprintf('%0.2f ', eval.confusion.per(row,col));
    end
    fprintf('\n');
end

fprintf('%s\n','Accuracy Evaluation Results');
fprintf('\tAverage System Accuracy(%%)   : %0.2f\n', eval.avgAccuracy)
fprintf('\tSystem Error(%%)              : %0.2f\n', eval.errRate)
fprintf('\tPrecision (Micro)(%%)         : %0.2f\n', eval.precisionMicro)
fprintf('\tRecall (Micro)(%%)            : %0.2f\n', eval.recallMicro)
fprintf('\tFscore (Micro)(%%)            : %0.2f\n', eval.fscoreMicro)
fprintf('\tPrecision (Macro)(%%)         : %0.2f\n', eval.precisionMacro)
fprintf('\tRecall (Macro)(%%)            : %0.2f\n', eval.recallMacro)
fprintf('\tFscore (Macro)(%%)            : %0.2f\n', eval.fscoreMacro)

Outputs

Confusion Results

Confusion value = 0.17
Confusion Matrix
    1.00 0.00 1.00 
    0.00 2.00 0.00 
    0.00 0.00 2.00 
Indices
    [2]	[]	[1]
    []	[3,4] 	[]
    []	[]	[5,6] 
Percentages
    0.20 0.00 1.00 0.80 
    0.00 0.00 1.00 1.00 
    0.00 0.33 0.67 1.00 

Accuracy Evaluation Results

    Average System Accuracy(%)   : 91.11
    System Error(%)              : 8.89
    Precision (Micro)(%)         : 88.89
    Recall (Micro)(%)            : 93.02
    Fscore (Micro)(%)            : 90.91
    Precision (Macro)(%)         : 88.89
    Recall (Macro)(%)            : 94.44
    Fscore (Macro)(%)            : 91.58

Note

For Java Implementation, visit accuracy-evaluation-java.

For CPP Implementation, visit accuracy-evaluation-cpp.

For Scala Implementation, visit accuracy-evaluation-scala.