Package smile.validation.metric
Class AUC
java.lang.Object
smile.validation.metric.AUC
 All Implemented Interfaces:
Serializable
,ProbabilisticClassificationMetric
The area under the curve (AUC). When using normalized units, the area under
the curve is equal to the probability that a classifier will rank a
randomly chosen positive instance higher than a randomly chosen negative
one (assuming 'positive' ranks higher than 'negative').
In statistics, a receiver operating characteristic (ROC), or ROC curve, is a graphical plot that illustrates the performance of a binary classifier system as its discrimination threshold is varied. The curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings.
AUC is quite noisy as a classification measure and has some other significant problems in model comparison.
We calculate AUC based on MannWhitney U test (https://en.wikipedia.org/wiki/MannWhitney_U_test).
 See Also:

Field Summary

Constructor Summary

Method Summary

Field Details

instance
Default instance.


Constructor Details

AUC
public AUC()


Method Details

score
public double score(int[] truth, double[] probability) Description copied from interface:ProbabilisticClassificationMetric
Returns a score to measure the quality of classification. Specified by:
score
in interfaceProbabilisticClassificationMetric
 Parameters:
truth
 the true class labels.probability
 The posterior probability of positive class. Returns:
 the metric.

of
public static double of(int[] truth, double[] probability) Calculates AUC for binary classifier. Parameters:
truth
 the ground truth.probability
 the posterior probability of positive class. Returns:
 AUC

toString
