binaryClassMetrics¶

Purpose¶

Computes statistics to assess the quality of binary predictions and prints out a report.

Format¶

out = binaryClassMetrics(y_true, y_predict)¶

Parameters

y_true – Nx1 vector of 0’s and 1’s, or strings that represent the true class labels.
y_predict – Nx1 vector of 0’s and 1’s, or strings that represent the predicted class labels.

Returns

out (struct) –

An instance of a binaryClassQuality structure. For an instance named out, the members are:

out.confusionMatrix	2x2 matrix, containing the computed confusion matrix.
out.accuracy	Scalar, range 0-1, the accuracy of the predicted labels.
out.precision	Scalar, (tp / (tp + fp)).
out.recall	Scalar, (tp / (tp + fn)).
out.fScore	Scalar, ((b^2 + 1) * tp) / ((b^2 + 1) * tp + b^2 * fn + fp) (b = 1) .
out.specificity	Scalar, (tp / (fp + tn)).
out.auc	Scalar, 0.5 * ((tp / (tp + fn) + (tp / (fp + tn)). Note: This is NOT the area under the roc curve, which requires requires predicted probabilities for its computation, rather than predicted class labels.

Example¶

Example 1: Basic use with binary labels¶

new;
library gml;

y_true = { 0, 0, 1, 0, 1, 1, 1, 0 };
y_pred = { 0, 0, 1, 0, 1, 0, 1, 0 };

call binaryClassMetrics(y_true, y_pred);

After the above code, the following report will be printed:

            Confusion matrix
            ----------------

    Class +        3       1
    Class -        0       4

   Accuracy            0.875
  Precision             0.75
     Recall                1
    F-score           0.8571
Specificity              0.8
        AUC              0.9

The interpretation of the confusion matrix is shown below:

         Confusion matrix
         ------------------------

Class +  (True  Pos)  (False Neg)
Class -  (False Pos)  (True  Neg)

If you want to store the statistics computed by binaryClassMetrics(), you can assign them to a binaryClassQuality structure like this:

/*
** Continuing with y_true and y_pred created above
*/

// Declare bqs to be a binaryClassQuality structure
struct binaryClassQuality bqs;

// Compute metrics and assign to struct
bqs = binaryClassMetrics(y_true, y_pred);

// Print some members
print "Accuracy = " bqs.accuracy;
print "F-score  = " bqs.fscore;

which will print the following output in addition to the standard report:

Accuracy =       0.87500000
F-score  =       0.85714287

Example 2: String class labels¶

new;
library gml;

string true_label = { "cat", "cat", "dog", "cat", "dog", "dog", "dog", "cat" };
string pred_label = { "cat", "cat", "dog", "cat", "dog", "cat", "dog", "cat" };

call binaryClassMetrics(true_label, pred_label);

After the above code, the following report will be printed:

            Confusion matrix
            ----------------

        cat        4       0
        dog        1       3

   Accuracy            0.875
  Precision                1
     Recall              0.8
    F-score           0.8889
Specificity                1
        AUC              0.9

GAUSS Machine Learning (GML)

cvSplit