/*
* File: BinaryConfusionMatrix.java
* Authors: Justin Basilico
* Company: Sandia National Laboratories
* Project: Cognitive Foundry Learning Core
*
* Copyright January 11, 2011, Sandia Corporation.
* Under the terms of Contract DE-AC04-94AL85000, there is a non-exclusive
* license for use of this work by or on behalf of the U.S. Government. Export
* of this program may require a license from the United States Government.
*/
package gov.sandia.cognition.learning.performance.categorization;
/**
* An interface for a binary confusion matrix. It is defined as a
* {@code ConfusionMatrix} over Boolean objects. It treats true as the positive
* category and false as the negative category.
*
* @author Justin Basilico
* @since 3.1
*/
public interface BinaryConfusionMatrix
extends ConfusionMatrix<Boolean>
{
/**
* Gets the number of true positives. This is the (true, true) entry.
*
* @return
* The number of true positives.
*/
public double getTruePositivesCount();
/**
* Gets the number of false positives. This is the (false, true) entry.
*
* @return
* The number of false positives.
*/
public double getFalsePositivesCount();
/**
* Gets the number of true negatives. This is the (false, false) entry.
*
* @return
* The number of true negatives.
*/
public double getTrueNegativesCount();
/**
* Gets the number of false negatives. This is the (true, false) entry.
*
* @return
* The number of false negatives.
*/
public double getFalseNegativesCount();
/**
* Gets the fraction of true positives. This is the (true, true) fraction.
*
* @return
* The fraction of true positives.
*/
public double getTruePositivesFraction();
/**
* Gets the fraction of false positives. This is the (false, true) fraction.
*
* @return
* The fraction of false positives.
*/
public double getFalsePositivesFraction();
/**
* Gets the fraction of true negatives. This is the (false, false) fraction.
*
* @return
* The fraction of true negatives.
*/
public double getTrueNegativesFraction();
/**
* Gets the fraction of false negatives. This is the (true, false) fraction.
*
* @return
* The fraction of false negatives.
*/
public double getFalseNegativesFraction();
/**
* The sensitivity value for the confusion matrix. The sensitivity is
* the number of true positives divided by the number of true positives plus
* the number of false negatives: TP / (TP + FN). It is equivalent to
* recall.
*
* @return
* The sensitivity, which is between 0.0 and 1.0.
*/
public double getSensitivity();
/**
* The specificity value for the confusion matrix. The specificity is
* the number of true negatives divided by the number of true negatives plus
* the number of false positives: TN / (TN + FP).
*
* @return
* The specificity value, which is between 0.0 and 1.0.
*/
public double getSpecificity();
/**
* The precision value for the confusion matrix. The precision is the
* number of true positives divided by the number of true positives plus
* the number of false positives: TP / (TP + FP).
*
* @return
* The precision value, which is between 0.0 and 1.0.
*/
public double getPrecision();
/**
* The recall value for the confusion matrix. The recall is the number of
* true positives divided by the number of true positives plus the number
* of false negatives: TP / (TP + FN). It is equivalent to sensitivity.
*
* @return
* The recall value, which is be between 0.0 and 1.0.
*/
public double getRecall();
/**
* The F-score of the confusion matrix, which is also known as the
* F1-score or F-measure. It is calculated as:
* 2 * (precision * recall) / (precision + recall)
* It is equivalent to the F-score with beta = 1.
*
* @return
* The F-score, which is between 0.0 and 1.0.
*/
public double getFScore();
/**
* The F-score for the confusion matrix with the given trade-off parameter
* (beta). It is calculated as:
* (1 + beta^2) * (precision * recall) / ((beta^2 * precision) + recall)
*
* @param beta
* The beta value of the score. It is the importance assigned to
* precision as compared to recall.
* @return
* The F-score for the matrix, which is greater than zero.
*/
public double getFScore(
final double beta);
}