Package smile.validation.metric

Class Sensitivity

java.lang.Object
smile.validation.metric.Sensitivity
All Implemented Interfaces:
Serializable, 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 Summary

    Fields
    Modifier and Type
    Field
    Description
    static final Sensitivity
    instance
    Default instance.

  • Constructor Summary

    Constructors
    Constructor
    Description
    Sensitivity()
     

  • Method Summary

    Modifier and Type
    Method
    Description
    static double
    of(int[] truth, int[] prediction)
    Calculates the sensitivity.
    double
    score(int[] truth, int[] prediction)
    Returns a score to measure the quality of classification.
    String
    toString()
     

    Methods inherited from class java.lang.Object

    clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

  • Field Details

    • instance

      public static final Sensitivity instance
      Default instance.

  • Constructor Details

    • Sensitivity

      public Sensitivity()

  • 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