gov.sandia.cognition.learning.performance.categorization
Interface BinaryConfusionMatrix

All Superinterfaces:
Cloneable, CloneableSerializable, ConfusionMatrix<Boolean>, Serializable
All Known Implementing Classes:
AbstractBinaryConfusionMatrix, DefaultBinaryConfusionMatrix

public interface BinaryConfusionMatrix
extends ConfusionMatrix<Boolean>

An interface for a binary confusion matrix. It is defined as a ConfusionMatrix over Boolean objects. It treats true as the positive category and false as the negative category.

Since:
3.1
Author:
Justin Basilico

Method Summary
 double getFalseNegativesCount()
          Gets the number of false negatives.
 double getFalseNegativesFraction()
          Gets the fraction of false negatives.
 double getFalsePositivesCount()
          Gets the number of false positives.
 double getFalsePositivesFraction()
          Gets the fraction of false positives.
 double getFScore()
          The F-score of the confusion matrix, which is also known as the F1-score or F-measure.
 double getFScore(double beta)
          The F-score for the confusion matrix with the given trade-off parameter (beta).
 double getPrecision()
          The precision value for the confusion matrix.
 double getRecall()
          The recall value for the confusion matrix.
 double getSensitivity()
          The sensitivity value for the confusion matrix.
 double getSpecificity()
          The specificity value for the confusion matrix.
 double getTrueNegativesCount()
          Gets the number of true negatives.
 double getTrueNegativesFraction()
          Gets the fraction of true negatives.
 double getTruePositivesCount()
          Gets the number of true positives.
 double getTruePositivesFraction()
          Gets the fraction of true positives.
 
Methods inherited from interface gov.sandia.cognition.learning.performance.categorization.ConfusionMatrix
add, add, addAll, clear, getAccuracy, getActualCategories, getActualCount, getAverageCategoryAccuracy, getAverageCategoryErrorRate, getCategories, getCategoryAccuracy, getCategoryErrorRate, getCount, getErrorRate, getPredictedCategories, getPredictedCategories, getPredictedCount, getTotalCorrectCount, getTotalCount, getTotalIncorrectCount, isEmpty
 
Methods inherited from interface gov.sandia.cognition.util.CloneableSerializable
clone
 

Method Detail

getTruePositivesCount

double getTruePositivesCount()
Gets the number of true positives. This is the (true, true) entry.

Returns:
The number of true positives.

getFalsePositivesCount

double getFalsePositivesCount()
Gets the number of false positives. This is the (false, true) entry.

Returns:
The number of false positives.

getTrueNegativesCount

double getTrueNegativesCount()
Gets the number of true negatives. This is the (false, false) entry.

Returns:
The number of true negatives.

getFalseNegativesCount

double getFalseNegativesCount()
Gets the number of false negatives. This is the (true, false) entry.

Returns:
The number of false negatives.

getTruePositivesFraction

double getTruePositivesFraction()
Gets the fraction of true positives. This is the (true, true) fraction.

Returns:
The fraction of true positives.

getFalsePositivesFraction

double getFalsePositivesFraction()
Gets the fraction of false positives. This is the (false, true) fraction.

Returns:
The fraction of false positives.

getTrueNegativesFraction

double getTrueNegativesFraction()
Gets the fraction of true negatives. This is the (false, false) fraction.

Returns:
The fraction of true negatives.

getFalseNegativesFraction

double getFalseNegativesFraction()
Gets the fraction of false negatives. This is the (true, false) fraction.

Returns:
The fraction of false negatives.

getSensitivity

double getSensitivity()
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.

Returns:
The sensitivity, which is between 0.0 and 1.0.

getSpecificity

double getSpecificity()
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).

Returns:
The specificity value, which is between 0.0 and 1.0.

getPrecision

double getPrecision()
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).

Returns:
The precision value, which is between 0.0 and 1.0.

getRecall

double getRecall()
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.

Returns:
The recall value, which is be between 0.0 and 1.0.

getFScore

double getFScore()
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.

Returns:
The F-score, which is between 0.0 and 1.0.

getFScore

double getFScore(double beta)
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)

Parameters:
beta - The beta value of the score. It is the importance assigned to precision as compared to recall.
Returns:
The F-score for the matrix, which is greater than zero.