smile.validation

Class Sensitivity

java.lang.Object

smile.validation.Sensitivity

All Implemented Interfaces:

ClassificationMeasure

public class Sensitivity
extends Object
implements ClassificationMeasure

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 all others are regarded as negative.

Author:

Haifeng Li

Constructor Summary

Constructors

Constructor and Description
Sensitivity()

Method Summary

Methods

Modifier and Type	Method and Description
double	measure(int[] truth, int[] prediction) Returns an index 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

Constructor Detail

Sensitivity

public Sensitivity()

Method Detail

measure

public double measure(int[] truth,
             int[] prediction)

Description copied from interface: ClassificationMeasure

Returns an index to measure the quality of classification.

Specified by:

measure in interface ClassificationMeasure

Parameters:

truth - the true class labels.

prediction - the predicted class labels.

toString

public String toString()

Overrides:

toString in class Object