Accuracy evaluation

A java 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 [A systematic analysis of performance measures for classification tasks] (http://www.sciencedirect.com/science/article/pii/S0306457309000259) and MATLAB confusion implementation.

Uses

  public static void main(String[] args) {
        // targets: SxQ (S:Classes; Q:Samples)
        // outputs: SxQ (S:Classes; Q:Samples)

        double[][] targets = {
                {1, 1, 0, 0, 0, 0},
                {0, 0, 1, 1, 0, 0},
                {0, 0, 0, 0, 1, 1}
        };
        double[][] 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}
        };

        Confusion confusion = new Confusion(targets, outputs);
        confusion.print();

        Evaluation evaluation = new Evaluation(confusion);
        evaluation.print();
  }

Output

Confusion Results

Confusion value
	c = 0.17
Confusion Matrix
	1 0 1 
	0 2 0 
	0 0 2 
Indices
	[1]		[]		[0]
	[]		[2,3]		[]
	[]		[]		[4,5]
Percentages
	0.2 0.0 1.0 0.8 
	0.0 0.0 1.0 1.0 
	0.0 0.33 0.67 1.0 

Accuracy Evaluation Results

Average Accuracy(%)       : 91.11
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 C++ Implementation, visit accuracy-evaluation-cpp

For MATLAB Implementation, visit accuracy-evaluation-matlab