Class Sensitivity

java.lang.Object
smile.validation.metric.Sensitivity
All Implemented Interfaces:
Serializable, ToDoubleBiFunction<int[],int[]>, ClassificationMetric
public class Sensitivity extends Object implements ClassificationMetric
Sensitivity or true positive rate (TPR) (also called hit rate, recall) is a statistical measures of the performance of a binary classification test. Sensitivity is the proportion of actual positives which are correctly identified as such. 
    TPR = TP / P = TP / (TP + FN)
Sensitivity and specificity are closely related to the concepts of type I and type II errors. For any test, there is usually a trade-off between the measures. This trade-off can be represented graphically using an ROC curve.

In this implementation, the class label 1 is regarded as positive and 0 is regarded as negative.

See Also:

  • Field Details

    • instance

      public static final Sensitivity instance
      Default instance.

  • Constructor Details

    • Sensitivity

      public Sensitivity()
      Constructor.

  • Method Details

    • score

      public double score(int[] truth, int[] prediction)
      Description copied from interface: ClassificationMetric
      Returns a score to measure the quality of classification.
      Specified by:
      score in interface ClassificationMetric
      Parameters:
      truth - the true class labels.
      prediction - the predicted class labels.
      Returns:
      the metric.

    • of

      public static double of(int[] truth, int[] prediction)
      Calculates the sensitivity.
      Parameters:
      truth - the ground truth.
      prediction - the prediction.
      Returns:
      the metric.

    • toString

      public String toString()
      Overrides:
      toString in class Object