binaryClassMetrics#

Purpose#

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

Format#

out = binaryClassMetrics(y_true, y_predict)#
out = binaryClassMetrics(df_true, df_predict, classes)
Parameters:

  • y_true (Nx1 dataframe, or string array.) – That represents the true class labels.

  • y_predict – That represents the predicted class labels.

  • df_true (Nx1 dataframe, or string array.) – That represents the true class labels.

  • y_predict – That represents the predicted class labels.

  • classes (String, 1x1 or 2x1 categorical dataframe, or string array.) – The first element of classes indicates which class should be treated as the positive case. This input is required if the true and predict inputs are string arrays or categorical dataframes.

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, \(\frac{tp}{tp + fp}\).

out.recall

Scalar, \(\frac{tp}{tp + fn}\)

out.fScore

Scalar, \(\frac{(b^2 + 1) * tp}{(b^2 + 1) * tp + b^2 * fn + fp)}\) (b = 1) .

out.specificity

Scalar, \(\frac{tp}{fp + tn}\)).

out.balancedAccuracy

Scalar, \(0.5 * (\frac{tp}{tp + fn} + \frac{tn}{tn + fp}\)).

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
==================================
                   Predicted class
                   ---------------
                         +       -
       True class
       ----------
            1 (+)        3       1
            0 (-)        0       4

         Accuracy            0.875
        Precision                1
           Recall             0.75
          F-score           0.8571
      Specificity                1
Balanced Accuracy            0.875

The interpretation of the confusion matrix is shown below:

========================================
                        Confusion matrix
========================================
                         Predicted class
                  ----------------------
                     +            -
   True class
   ----------
    Class (+)    (True Pos)  (False Neg)
    Class (-)   (False Pos)   (True Neg)

You can store the statistics computed by binaryClassMetrics(), using 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: Dataframe inputs#

new;
library gml;

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

// Create dataframes
df_true = asDF(true_label, "Observed");
df_pred = asDF(pred_label, "Prediction");

call binaryClassMetrics(df_true, df_pred, "cat");

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

==================================
                  Confusion matrix
==================================
                   Predicted class
                   ---------------
                         +       -
       True class
       ----------
          cat (+)        4       0
          dog (-)        1       3

         Accuracy            0.875
        Precision              0.8
           Recall                1
          F-score           0.8889
      Specificity             0.75
Balanced Accuracy            0.875

Example 3: 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, "dog");

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

==================================
                  Confusion matrix
==================================
                   Predicted class
                   ---------------
                         +       -
       True class
       ----------
          dog (+)        3       1
          cat (-)        0       4

         Accuracy            0.875
        Precision                1
           Recall             0.75
          F-score           0.8571
      Specificity                1
Balanced Accuracy            0.875
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