receiver operating characteristic curve
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出典(authority):フリー百科事典『ウィキペディア（Wikipedia）』「2017/11/07 23:21:03」(JST)
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[Wiki en表示]In statistics, a receiver operating characteristic curve, i.e. ROC curve, is a graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied.
The ROC curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings. The truepositive rate is also known as sensitivity, recall or probability of detection^{[1]} in machine learning. The falsepositive rate is also known as the fallout or probability of false alarm^{[1]} and can be calculated as (1 − specificity). The ROC curve is thus the sensitivity as a function of fallout. In general, if the probability distributions for both detection and false alarm are known, the ROC curve can be generated by plotting the cumulative distribution function (area under the probability distribution from $\infty$ to the discrimination threshold) of the detection probability in the yaxis versus the cumulative distribution function of the falsealarm probability on the xaxis.
ROC analysis provides tools to select possibly optimal models and to discard suboptimal ones independently from (and prior to specifying) the cost context or the class distribution. ROC analysis is related in a direct and natural way to cost/benefit analysis of diagnostic decision making.
The ROC curve was first developed by electrical engineers and radar engineers during World War II for detecting enemy objects in battlefields and was soon introduced to psychology to account for perceptual detection of stimuli. ROC analysis since then has been used in medicine, radiology, biometrics, forecasting of natural hazards^{[2]}, meteorology^{[3]}, model performance assessment^{[4]}, and other areas for many decades and is increasingly used in machine learning and data mining research.
The ROC is also known as a relative operating characteristic curve, because it is a comparison of two operating characteristics (TPR and FPR) as the criterion changes.^{[5]}
Contents
 1 Basic concept
 2 ROC space
 3 Curves in ROC space
 4 Further interpretations
 4.1 Area under the curve
 4.2 Other measures
 5 Detection error tradeoff graph
 6 Zscore
 7 History
 8 ROC curves beyond binary classification
 9 See also
 10 References
 11 Further reading
Basic concept
A classification model (classifier or diagnosis) is a mapping of instances between certain classes/groups. The classifier or diagnosis result can be a real value (continuous output), in which case the classifier boundary between classes must be determined by a threshold value (for instance, to determine whether a person has hypertension based on a blood pressure measure). Or it can be a discrete class label, indicating one of the classes.
Let us consider a twoclass prediction problem (binary classification), in which the outcomes are labeled either as positive (p) or negative (n). There are four possible outcomes from a binary classifier. If the outcome from a prediction is p and the actual value is also p, then it is called a true positive (TP); however if the actual value is n then it is said to be a false positive (FP). Conversely, a true negative (TN) has occurred when both the prediction outcome and the actual value are n, and false negative (FN) is when the prediction outcome is n while the actual value is p.
To get an appropriate example in a realworld problem, consider a diagnostic test that seeks to determine whether a person has a certain disease. A false positive in this case occurs when the person tests positive, but does not actually have the disease. A false negative, on the other hand, occurs when the person tests negative, suggesting they are healthy, when they actually do have the disease.
Let us define an experiment from P positive instances and N negative instances for some condition. The four outcomes can be formulated in a 2×2 contingency table or confusion matrix, as follows:
True condition  
Total population  Condition positive  Condition negative  Prevalence = Σ Condition positive/Σ Total population  Accuracy (ACC) = Σ True positive + Σ True negative/Σ Total population  
Predicted condition 
Predicted condition positive 
True positive  False positive, Type I error 
Positive predictive value (PPV), Precision = Σ True positive/Σ Predicted condition positive  False discovery rate (FDR), probability of false alarm = Σ False positive/Σ Predicted condition positive  
Predicted condition negative 
False negative, Type II error 
True negative  False omission rate (FOR) = Σ False negative/Σ Predicted condition negative  Negative predictive value (NPV) = Σ True negative/Σ Predicted condition negative  
Click thumbnail for interactive chart:

True positive rate (TPR), Recall, Sensitivity, probability of detection = Σ True positive/Σ Condition positive  False positive rate (FPR), Fallout= Σ False positive/Σ Condition negative  Positive likelihood ratio (LR+) = TPR/FPR  Diagnostic odds ratio (DOR) = LR+/LR−  F_{1} score = 2/1/Recall + 1/Precision  
False negative rate (FNR), Miss rate = Σ False negative/Σ Condition positive  True negative rate (TNR), Specificity (SPC) = Σ True negative/Σ Condition negative  Negative likelihood ratio (LR−) = FNR/TNR 
ROC space
The contingency table can derive several evaluation "metrics" (see infobox). To draw a ROC curve, only the true positive rate (TPR) and false positive rate (FPR) are needed (as functions of some classifier parameter). The TPR defines how many correct positive results occur among all positive samples available during the test. FPR, on the other hand, defines how many incorrect positive results occur among all negative samples available during the test.
A ROC space is defined by FPR and TPR as x and y axes, respectively, which depicts relative tradeoffs between true positive (benefits) and false positive (costs). Since TPR is equivalent to sensitivity and FPR is equal to 1 − specificity, the ROC graph is sometimes called the sensitivity vs (1 − specificity) plot. Each prediction result or instance of a confusion matrix represents one point in the ROC space.
The best possible prediction method would yield a point in the upper left corner or coordinate (0,1) of the ROC space, representing 100% sensitivity (no false negatives) and 100% specificity (no false positives). The (0,1) point is also called a perfect classification. A random guess would give a point along a diagonal line (the socalled line of nodiscrimination) from the left bottom to the top right corners (regardless of the positive and negative base rates). An intuitive example of random guessing is a decision by flipping coins. As the size of the sample increases, a random classifier's ROC point migrates towards the diagonal line. In the case of a balanced coin, it will migrate to the point (0.5, 0.5).
The diagonal divides the ROC space. Points above the diagonal represent good classification results (better than random), points below the line represent poor results (worse than random). Note that the output of a consistently poor predictor could simply be inverted to obtain a good predictor.
Let us look into four prediction results from 100 positive and 100 negative instances (please keep in mind that the table layout is flipped compared to the table above):
A  B  C  C′  






TPR = 0.63  TPR = 0.77  TPR = 0.24  TPR = 0.76  
FPR = 0.28  FPR = 0.77  FPR = 0.88  FPR = 0.12  
PPV = 0.69  PPV = 0.50  PPV = 0.21  PPV = 0.86  
F1 = 0.66  F1 = 0.61  F1 = 0.22  F1 = 0.81  
ACC = 0.68  ACC = 0.50  ACC = 0.18  ACC = 0.82 
Plots of the four results above in the ROC space are given in the figure. The result of method A clearly shows the best predictive power among A, B, and C. The result of B lies on the random guess line (the diagonal line), and it can be seen in the table that the accuracy of B is 50%. However, when C is mirrored across the center point (0.5,0.5), the resulting method C′ is even better than A. This mirrored method simply reverses the predictions of whatever method or test produced the C contingency table. Although the original C method has negative predictive power, simply reversing its decisions leads to a new predictive method C′ which has positive predictive power. When the C method predicts p or n, the C′ method would predict n or p, respectively. In this manner, the C′ test would perform the best. The closer a result from a contingency table is to the upper left corner, the better it predicts, but the distance from the random guess line in either direction is the best indicator of how much predictive power a method has. If the result is below the line (i.e. the method is worse than a random guess), all of the method's predictions must be reversed in order to utilize its power, thereby moving the result above the random guess line.
Curves in ROC space
In binary classification, the class prediction for each instance is often made based on a continuous random variable $X$, which is a "score" computed for the instance (e.g. estimated probability in logistic regression). Given a threshold parameter $T$, the instance is classified as "positive" if $X>T$, and "negative" otherwise. $X$ follows a probability density $f_{1}(x)$ if the instance actually belongs to class "positive", and $f_{0}(x)$ if otherwise. Therefore, the true positive rate is given by ${\mbox{TPR}}(T)=\int _{T}^{\infty }f_{1}(x)\,dx$ and the false positive rate is given by ${\mbox{FPR}}(T)=\int _{T}^{\infty }f_{0}(x)\,dx$. The ROC curve plots parametrically TPR(T) versus FPR(T) with T as the varying parameter.
For example, imagine that the blood protein levels in diseased people and healthy people are normally distributed with means of 2 g/dL and 1 g/dL respectively. A medical test might measure the level of a certain protein in a blood sample and classify any number above a certain threshold as indicating disease. The experimenter can adjust the threshold (black vertical line in the figure), which will in turn change the false positive rate. Increasing the threshold would result in fewer false positives (and more false negatives), corresponding to a leftward movement on the curve. The actual shape of the curve is determined by how much overlap the two distributions have. These concepts are demonstrated in the Receiver Operating Characteristic (ROC) Curves Applet.
Further interpretations
Sometimes, the ROC is used to generate a summary statistic. Common versions are:
 the intercept of the ROC curve with the line at 45 degrees orthogonal to the nodiscrimination line  the balance point where Sensitivity = Specificity
 the intercept of the ROC curve with the tangent at 45 degrees parallel to the nodiscrimination line that is closest to the errorfree point (0,1)  also called Youden's J statistic and generalized as Informedness^{[6]}
 the area between the ROC curve and the nodiscrimination line  Gini Coefficient
 the area between the full ROC curve and the triangular ROC curve including only (0,0), (1,1) and one selected operating point (tpr,fpr)  Consistency^{[7]}
 the area under the ROC curve, or "AUC" ("Area Under Curve"), or A' (pronounced "aprime"),^{[8]} or "cstatistic".^{[9]}
 the sensitivity index d' (pronounced "dprime"), the distance between the mean of the distribution of activity in the system under noisealone conditions and its distribution under signalalone conditions, divided by their standard deviation, under the assumption that both these distributions are normal with the same standard deviation. Under these assumptions, the shape of the ROC is entirely determined by d'.
However, any attempt to summarize the ROC curve into a single number loses information about the pattern of tradeoffs of the particular discriminator algorithm.
Area under the curve
When using normalized units, the area under the curve (often referred to as simply the AUC) 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').^{[10]} This can be seen as follows: the area under the curve is given by (the integral boundaries are reversed as large T has a lower value on the xaxis)
 $$
A = ∫ ∞ − ∞ TPR ( T ) ( − FPR ′ ( T ) ) d T = ∫ − ∞ ∞ ∫ − ∞ ∞ I ( T ′ > T ) f 1 ( T ′ ) f 0 ( T ) d T ′ d T = P ( X 1 > X 0 ) {\displaystyle A=\int _{\infty }^{\infty }{\mbox{TPR}}(T)\left({\mbox{FPR}}'(T)\right)\,dT=\int _{\infty }^{\infty }\int _{\infty }^{\infty }I(T'>T)f_{1}(T')f_{0}(T)\,dT'\,dT=P(X_{1}>X_{0})}
where $X_{1}$ is the score for a positive instance and $X_{0}$ is the score for a negative instance, and $f_{0}$ and $f_{1}$ are probability densities as defined in previous section.
It can further be shown that the AUC is closely related to the Mann–Whitney U,^{[11]}^{[12]} which tests whether positives are ranked higher than negatives. It is also equivalent to the Wilcoxon test of ranks.^{[12]} The AUC is related to the Gini coefficient ($G_{1}$) by the formula $G_{1}=2{\mbox{AUC}}1$, where:
 $$
G 1 = 1 − ∑ k = 1 n ( X k − X k − 1 ) ( Y k + Y k − 1 ) {\displaystyle G_{1}=1\sum _{k=1}^{n}(X_{k}X_{k1})(Y_{k}+Y_{k1})} ^{[13]}
In this way, it is possible to calculate the AUC by using an average of a number of trapezoidal approximations.
It is also common to calculate the Area Under the ROC Convex Hull (ROC AUCH = ROCH AUC) as any point on the line segment between two prediction results can be achieved by randomly using one or other system with probabilities proportional to the relative length of the opposite component of the segment.^{[14]} Interestingly, it is also possible to invert concavities – just as in the figure the worse solution can be reflected to become a better solution; concavities can be reflected in any line segment, but this more extreme form of fusion is much more likely to overfit the data.^{[15]}
The machine learning community most often uses the ROC AUC statistic for model comparison.^{[16]} However, this practice has recently been questioned based upon new machine learning research that shows that the AUC is quite noisy as a classification measure^{[17]} and has some other significant problems in model comparison.^{[18]}^{[19]} A reliable and valid AUC estimate can be interpreted as the probability that the classifier will assign a higher score to a randomly chosen positive example than to a randomly chosen negative example. However, the critical research^{[17]}^{[18]} suggests frequent failures in obtaining reliable and valid AUC estimates. Thus, the practical value of the AUC measure has been called into question,^{[19]} raising the possibility that the AUC may actually introduce more uncertainty into machine learning classification accuracy comparisons than resolution. Nonetheless, the coherence of AUC as a measure of aggregated classification performance has been vindicated, in terms of a uniform rate distribution,^{[20]} and AUC has been linked to a number of other performance metrics such as the Brier score.^{[21]}
One recent explanation of the problem with ROC AUC is that reducing the ROC Curve to a single number ignores the fact that it is about the tradeoffs between the different systems or performance points plotted and not the performance of an individual system, as well as ignoring the possibility of concavity repair, so that related alternative measures such as Informedness^{[6]} or DeltaP are recommended.^{[22]} These measures are essentially equivalent to the Gini for a single prediction point with DeltaP' = Informedness = 2AUC1, whilst DeltaP = Markedness represents the dual (viz. predicting the prediction from the real class) and their geometric mean is the Matthews correlation coefficient.^{[6]}
Other measures
Whereas ROC AUC varies between 0 and 1 — with an uninformative classifier yielding 0.5 — the alternative measures Informedness^{[6]} and Gini Coefficient (in the single parameterization or single system case)^{[6]} all have the advantage that 0 represents chance performance whilst 1 represents perfect performance, and −1 represents the "perverse" case of full informedness always giving the wrong response.^{[23]}. Bringing chance performance to 0 allows these alternative scales to be interpreted as Kappa statistics. Informedness has been shown to have desirable characteristics for Machine Learning versus other common definitions of Kappa such as Cohen Kappa and Fleiss Kappa.^{[6]}^{[24]}
Sometimes it can be more useful to look at a specific region of the ROC Curve rather than at the whole curve. It is possible to compute partial AUC.^{[25]} For example, one could focus on the region of the curve with low false positive rate, which is often of prime interest for population screening tests.^{[26]} Another common approach for classification problems in which P ≪ N (common in bioinformatics applications) is to use a logarithmic scale for the xaxis.^{[27]}
Detection error tradeoff graph
An alternative to the ROC curve is the detection error tradeoff (DET) graph, which plots the false negative rate (missed detections) vs. the false positive rate (false alarms) on nonlinearly transformed x and yaxes. The transformation function is the quantile function of the normal distribution, i.e., the inverse of the cumulative normal distribution. It is, in fact, the same transformation as zROC, below, except that the complement of the hit rate, the miss rate or false negative rate, is used. This alternative spends more graph area on the region of interest. Most of the ROC area is of little interest; one primarily cares about the region tight against the yaxis and the top left corner – which, because of using miss rate instead of its complement, the hit rate, is the lower left corner in a DET plot. Furthermore, DET graphs have the useful property of linearity and a linear threshold behavior for normal distributions.^{[28]} The DET plot is used extensively in the automatic speaker recognition community, where the name DET was first used. The analysis of the ROC performance in graphs with this warping of the axes was used by psychologists in perception studies halfway through the 20th century, where this was dubbed "double probability paper".^{[citation needed]}
Zscore
If a standard score is applied to the ROC curve, the curve will be transformed into a straight line.^{[29]} This zscore is based on a normal distribution with a mean of zero and a standard deviation of one. In memory strength theory, one must assume that the zROC is not only linear, but has a slope of 1.0. The normal distributions of targets (studied objects that the subjects need to recall) and lures (non studied objects that the subjects attempt to recall) is the factor causing the zROC to be linear.
The linearity of the zROC curve depends on the standard deviations of the target and lure strength distributions. If the standard deviations are equal, the slope will be 1.0. If the standard deviation of the target strength distribution is larger than the standard deviation of the lure strength distribution, then the slope will be smaller than 1.0. In most studies, it has been found that the zROC curve slopes constantly fall below 1, usually between 0.5 and 0.9.^{[30]} Many experiments yielded a zROC slope of 0.8. A slope of 0.8 implies that the variability of the target strength distribution is 25% larger than the variability of the lure strength distribution.^{[31]}
Another variable used is d' (d prime) (discussed above in "Other measures"), which can easily be expressed in terms of zvalues. Although d' is a commonly used parameter, it must be recognized that it is only relevant when strictly adhering to the very strong assumptions of strength theory made above.^{[32]}
The zscore of an ROC curve is always linear, as assumed, except in special situations. The Yonelinas familiarityrecollection model is a twodimensional account of recognition memory. Instead of the subject simply answering yes or no to a specific input, the subject gives the input a feeling of familiarity, which operates like the original ROC curve. What changes, though, is a parameter for Recollection (R). Recollection is assumed to be allornone, and it trumps familiarity. If there were no recollection component, zROC would have a predicted slope of 1. However, when adding the recollection component, the zROC curve will be concave up, with a decreased slope. This difference in shape and slope result from an added element of variability due to some items being recollected. Patients with anterograde amnesia are unable to recollect, so their Yonelinas zROC curve would have a slope close to 1.0.^{[33]}
History
The ROC curve was first used during World War II for the analysis of radar signals before it was employed in signal detection theory.^{[34]} Following the attack on Pearl Harbor in 1941, the United States army began new research to increase the prediction of correctly detected Japanese aircraft from their radar signals. For this purposes they measured the ability of radar receiver operators to make these important distinctions, which was called the Receiver Operating Characteristics.^{[35]}
In the 1950s, ROC curves were employed in psychophysics to assess human (and occasionally nonhuman animal) detection of weak signals.^{[34]} In medicine, ROC analysis has been extensively used in the evaluation of diagnostic tests.^{[36]}^{[37]} ROC curves are also used extensively in epidemiology and medical research and are frequently mentioned in conjunction with evidencebased medicine. In radiology, ROC analysis is a common technique to evaluate new radiology techniques.^{[38]} In the social sciences, ROC analysis is often called the ROC Accuracy Ratio, a common technique for judging the accuracy of default probability models. ROC curves are widely used in laboratory medicine to assess diagnostic accuracy of a test, to choose the optimal cutoff of a test and to compare diagnostic accuracy of several tests.
ROC curves also proved useful for the evaluation of machine learning techniques. The first application of ROC in machine learning was by Spackman who demonstrated the value of ROC curves in comparing and evaluating different classification algorithms.^{[39]}
ROC curves beyond binary classification
The extension of ROC curves for classification problems with more than two classes has always been cumbersome, as the degrees of freedom increase quadratically with the number of classes, and the ROC space has $c(c1)$ dimensions, where $c$ is the number of classes.^{[40]} Some approaches have been made for the particular case with three classes (threeway ROC).^{[41]} The calculation of the volume under the ROC surface (VUS) has been analyzed and studied as a performance metric for multiclass problems.^{[42]} However, because of the complexity of approximating the true VUS, some other approaches ^{[43]} based on an extension of AUC are more popular as an evaluation metric.
Given the success of ROC curves for the assessment of classification models, the extension of ROC curves for other supervised tasks has also been investigated. Notable proposals for regression problems are the socalled regression error characteristic (REC) Curves ^{[44]} and the Regression ROC (RROC) curves.^{[45]} In the latter, RROC curves become extremely similar to ROC curves for classification, with the notions of asymmetry, dominance and convex hull. Also, the area under RROC curves is proportional to the error variance of the regression model.
See also
Sources: Fawcett (2006), Powers (2011), and Ting (2011) ^{[46]} ^{[47]} ^{[48]} 
 Statistics portal
Wikimedia Commons has media related to Receiver operating characteristic. 
 Brier score
 Coefficient of determination
 Constant false alarm rate
 Detection error tradeoff
 Detection theory
 F1 score
 False alarm
 Precision and recall
 ROCCET
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 ^ Ting, Kai Ming (2011). Encyclopedia of machine learning. Springer. ISBN 9780387301648.
Further reading
 Balakrishnan, Narayanaswamy (1991); Handbook of the Logistic Distribution, Marcel Dekker, Inc., ISBN 9780824785871
 Brown, Christopher D.; Davis, Herbert T. (2006). "Receiver operating characteristic curves and related decision measures: a tutorial". Chemometrics and Intelligent Laboratory Systems. 80: 24–38. doi:10.1016/j.chemolab.2005.05.004.
 Rotello, Caren M.; Heit, Evan; Dubé, Chad (2014). "When more data steer us wrong: replications with the wrong dependent measure perpetuate erroneous conclusions" (PDF). Psychonomic Bulletin & Review. 22: 944–954. doi:10.3758/s1342301407592.
 Fawcett, Tom (2004). "ROC Graphs: Notes and Practical Considerations for Researchers" (PDF). Pattern Recognition Letters. 27 (8): 882–891.
 Gonen, Mithat (2007); Analyzing Receiver Operating Characteristic Curves Using SAS, SAS Press, ISBN 9781599942988
 Green, William H., (2003) Econometric Analysis, fifth edition, Prentice Hall, ISBN 0130661899
 Heagerty, Patrick J.; Lumley, Thomas; and Pepe, Margaret S. (2000); Timedependent ROC Curves for Censored Survival Data and a Diagnostic Marker, Biometrics, 56:337–344
 Hosmer, David W.; and Lemeshow, Stanley (2000); Applied Logistic Regression, 2nd ed., New York, NY: Wiley, ISBN 0471356328
 Lasko, Thomas A.; Bhagwat, Jui G.; Zou, Kelly H.; and OhnoMachado, Lucila (2005); The use of receiver operating characteristic curves in biomedical informatics, Journal of Biomedical Informatics, 38(5):404–415
 Stephan, Carsten; Wesseling, Sebastian; Schink, Tania; and Jung, Klaus (2003); Comparison of Eight Computer Programs for ReceiverOperating Characteristic Analysis, Clinical Chemistry, 49:433–439
 Swets, John A.; Dawes, Robyn M.; and Monahan, John (2000); Better Decisions through Science, Scientific American, October, pp. 82–87
 Zou, Kelly H.; O'Malley, A. James; Mauri, Laura (2007); Receiveroperating characteristic analysis for evaluating diagnostic tests and predictive models, Circulation, 115(5):654–7
 Zhou, XiaoHua; Obuchowski, Nancy A.; McClish, Donna K. (2002). Statistical Methods in Diagnostic Medicine. New York, NY: Wiley & Sons. ISBN 9780471347729.
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出典(authority):フリー百科事典『ウィキペディア（Wikipedia）』「2018/02/11 04:24:31」(JST)
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[Wiki en表示]In statistics, a receiver operating characteristic curve, i.e. ROC curve, is a graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied.
The ROC curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings. The truepositive rate is also known as sensitivity, recall or probability of detection^{[1]} in machine learning. The falsepositive rate is also known as the fallout or probability of false alarm^{[1]} and can be calculated as (1 − specificity). It can also be thought of as a plot of the Power as a function of the Type I Error of the decision rule (when the performance is calculated from just a sample of the population, it can be thought of as estimators of these quantities). The ROC curve is thus the sensitivity as a function of fallout. In general, if the probability distributions for both detection and false alarm are known, the ROC curve can be generated by plotting the cumulative distribution function (area under the probability distribution from $\infty$ to the discrimination threshold) of the detection probability in the yaxis versus the cumulative distribution function of the falsealarm probability on the xaxis.
ROC analysis provides tools to select possibly optimal models and to discard suboptimal ones independently from (and prior to specifying) the cost context or the class distribution. ROC analysis is related in a direct and natural way to cost/benefit analysis of diagnostic decision making.
The ROC curve was first developed by electrical engineers and radar engineers during World War II for detecting enemy objects in battlefields and was soon introduced to psychology to account for perceptual detection of stimuli. ROC analysis since then has been used in medicine, radiology, biometrics, forecasting of natural hazards,^{[2]} meteorology,^{[3]} model performance assessment,^{[4]} and other areas for many decades and is increasingly used in machine learning and data mining research.
The ROC is also known as a relative operating characteristic curve, because it is a comparison of two operating characteristics (TPR and FPR) as the criterion changes.^{[5]}
Contents
 1 Basic concept
 2 ROC space
 3 Curves in ROC space
 4 Further interpretations
 4.1 Area under the curve
 4.2 Other measures
 5 Detection error tradeoff graph
 6 Zscore
 7 History
 8 ROC curves beyond binary classification
 9 See also
 10 References
 11 External links
 12 Further reading
Basic concept
A classification model (classifier or diagnosis) is a mapping of instances between certain classes/groups. The classifier or diagnosis result can be a real value (continuous output), in which case the classifier boundary between classes must be determined by a threshold value (for instance, to determine whether a person has hypertension based on a blood pressure measure). Or it can be a discrete class label, indicating one of the classes.
Let us consider a twoclass prediction problem (binary classification), in which the outcomes are labeled either as positive (p) or negative (n). There are four possible outcomes from a binary classifier. If the outcome from a prediction is p and the actual value is also p, then it is called a true positive (TP); however if the actual value is n then it is said to be a false positive (FP). Conversely, a true negative (TN) has occurred when both the prediction outcome and the actual value are n, and false negative (FN) is when the prediction outcome is n while the actual value is p.
To get an appropriate example in a realworld problem, consider a diagnostic test that seeks to determine whether a person has a certain disease. A false positive in this case occurs when the person tests positive, but does not actually have the disease. A false negative, on the other hand, occurs when the person tests negative, suggesting they are healthy, when they actually do have the disease.
Let us define an experiment from P positive instances and N negative instances for some condition. The four outcomes can be formulated in a 2×2 contingency table or confusion matrix, as follows:
True condition  
Total population  Condition positive  Condition negative  Prevalence = Σ Condition positive/Σ Total population  Accuracy (ACC) = Σ True positive + Σ True negative/Σ Total population  
Predicted condition 
Predicted condition positive 
True positive, Power 
False positive, Type I error 
Positive predictive value (PPV), Precision = Σ True positive/Σ Predicted condition positive  False discovery rate (FDR) = Σ False positive/Σ Predicted condition positive  
Predicted condition negative 
False negative, Type II error 
True negative  False omission rate (FOR) = Σ False negative/Σ Predicted condition negative  Negative predictive value (NPV) = Σ True negative/Σ Predicted condition negative  
True positive rate (TPR), Recall, Sensitivity, probability of detection = Σ True positive/Σ Condition positive  False positive rate (FPR), Fallout, probability of false alarm = Σ False positive/Σ Condition negative  Positive likelihood ratio (LR+) = TPR/FPR  Diagnostic odds ratio (DOR) = LR+/LR−  F_{1} score = 2/1/Recall + 1/Precision  
False negative rate (FNR), Miss rate = Σ False negative/Σ Condition positive  True negative rate (TNR), Specificity (SPC) = Σ True negative/Σ Condition negative  Negative likelihood ratio (LR−) = FNR/TNR 
ROC space
The contingency table can derive several evaluation "metrics" (see infobox). To draw a ROC curve, only the true positive rate (TPR) and false positive rate (FPR) are needed (as functions of some classifier parameter). The TPR defines how many correct positive results occur among all positive samples available during the test. FPR, on the other hand, defines how many incorrect positive results occur among all negative samples available during the test.
A ROC space is defined by FPR and TPR as x and y axes, respectively, which depicts relative tradeoffs between true positive (benefits) and false positive (costs). Since TPR is equivalent to sensitivity and FPR is equal to 1 − specificity, the ROC graph is sometimes called the sensitivity vs (1 − specificity) plot. Each prediction result or instance of a confusion matrix represents one point in the ROC space.
The best possible prediction method would yield a point in the upper left corner or coordinate (0,1) of the ROC space, representing 100% sensitivity (no false negatives) and 100% specificity (no false positives). The (0,1) point is also called a perfect classification. A random guess would give a point along a diagonal line (the socalled line of nodiscrimination) from the left bottom to the top right corners (regardless of the positive and negative base rates). An intuitive example of random guessing is a decision by flipping coins. As the size of the sample increases, a random classifier's ROC point migrates towards the diagonal line. In the case of a balanced coin, it will migrate to the point (0.5, 0.5).
The diagonal divides the ROC space. Points above the diagonal represent good classification results (better than random), points below the line represent poor results (worse than random). Note that the output of a consistently poor predictor could simply be inverted to obtain a good predictor.
Let us look into four prediction results from 100 positive and 100 negative instances (please keep in mind that the table layout is flipped compared to the table above):
A  B  C  C′  






TPR = 0.63  TPR = 0.77  TPR = 0.24  TPR = 0.76  
FPR = 0.28  FPR = 0.77  FPR = 0.88  FPR = 0.12  
PPV = 0.69  PPV = 0.50  PPV = 0.21  PPV = 0.86  
F1 = 0.66  F1 = 0.61  F1 = 0.22  F1 = 0.81  
ACC = 0.68  ACC = 0.50  ACC = 0.18  ACC = 0.82 
Plots of the four results above in the ROC space are given in the figure. The result of method A clearly shows the best predictive power among A, B, and C. The result of B lies on the random guess line (the diagonal line), and it can be seen in the table that the accuracy of B is 50%. However, when C is mirrored across the center point (0.5,0.5), the resulting method C′ is even better than A. This mirrored method simply reverses the predictions of whatever method or test produced the C contingency table. Although the original C method has negative predictive power, simply reversing its decisions leads to a new predictive method C′ which has positive predictive power. When the C method predicts p or n, the C′ method would predict n or p, respectively. In this manner, the C′ test would perform the best. The closer a result from a contingency table is to the upper left corner, the better it predicts, but the distance from the random guess line in either direction is the best indicator of how much predictive power a method has. If the result is below the line (i.e. the method is worse than a random guess), all of the method's predictions must be reversed in order to utilize its power, thereby moving the result above the random guess line.
Curves in ROC space
In binary classification, the class prediction for each instance is often made based on a continuous random variable $X$, which is a "score" computed for the instance (e.g. estimated probability in logistic regression). Given a threshold parameter $T$, the instance is classified as "positive" if $X>T$, and "negative" otherwise. $X$ follows a probability density $f_{1}(x)$ if the instance actually belongs to class "positive", and $f_{0}(x)$ if otherwise. Therefore, the true positive rate is given by ${\mbox{TPR}}(T)=\int _{T}^{\infty }f_{1}(x)\,dx$ and the false positive rate is given by ${\mbox{FPR}}(T)=\int _{T}^{\infty }f_{0}(x)\,dx$. The ROC curve plots parametrically TPR(T) versus FPR(T) with T as the varying parameter.
For example, imagine that the blood protein levels in diseased people and healthy people are normally distributed with means of 2 g/dL and 1 g/dL respectively. A medical test might measure the level of a certain protein in a blood sample and classify any number above a certain threshold as indicating disease. The experimenter can adjust the threshold (black vertical line in the figure), which will in turn change the false positive rate. Increasing the threshold would result in fewer false positives (and more false negatives), corresponding to a leftward movement on the curve. The actual shape of the curve is determined by how much overlap the two distributions have. These concepts are demonstrated in the Receiver Operating Characteristic (ROC) Curves Applet.
Further interpretations
Sometimes, the ROC is used to generate a summary statistic. Common versions are:
 the intercept of the ROC curve with the line at 45 degrees orthogonal to the nodiscrimination line  the balance point where Sensitivity = Specificity
 the intercept of the ROC curve with the tangent at 45 degrees parallel to the nodiscrimination line that is closest to the errorfree point (0,1)  also called Youden's J statistic and generalized as Informedness^{[6]}
 the area between the ROC curve and the nodiscrimination line  Gini Coefficient
 the area between the full ROC curve and the triangular ROC curve including only (0,0), (1,1) and one selected operating point (tpr,fpr)  Consistency^{[7]}
 the area under the ROC curve, or "AUC" ("Area Under Curve"), or A' (pronounced "aprime"),^{[8]} or "cstatistic".^{[9]}
 the sensitivity index d' (pronounced "dprime"), the distance between the mean of the distribution of activity in the system under noisealone conditions and its distribution under signalalone conditions, divided by their standard deviation, under the assumption that both these distributions are normal with the same standard deviation. Under these assumptions, the shape of the ROC is entirely determined by d'.
However, any attempt to summarize the ROC curve into a single number loses information about the pattern of tradeoffs of the particular discriminator algorithm.
Area under the curve
When using normalized units, the area under the curve (often referred to as simply the AUC) 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').^{[10]} This can be seen as follows: the area under the curve is given by (the integral boundaries are reversed as large T has a lower value on the xaxis)
 $$
A = ∫ ∞ − ∞ TPR ( T ) ( − FPR ′ ( T ) ) d T = ∫ − ∞ ∞ ∫ − ∞ ∞ I ( T ′ > T ) f 1 ( T ′ ) f 0 ( T ) d T ′ d T = P ( X 1 > X 0 ) {\displaystyle A=\int _{\infty }^{\infty }{\mbox{TPR}}(T)\left({\mbox{FPR}}'(T)\right)\,dT=\int _{\infty }^{\infty }\int _{\infty }^{\infty }I(T'>T)f_{1}(T')f_{0}(T)\,dT'\,dT=P(X_{1}>X_{0})}
where $X_{1}$ is the score for a positive instance and $X_{0}$ is the score for a negative instance, and $f_{0}$ and $f_{1}$ are probability densities as defined in previous section.
It can further be shown that the AUC is closely related to the Mann–Whitney U,^{[11]}^{[12]} which tests whether positives are ranked higher than negatives. It is also equivalent to the Wilcoxon test of ranks.^{[12]} The AUC is related to the Gini coefficient ($G_{1}$) by the formula $G_{1}=2{\mbox{AUC}}1$, where:
 $$
G 1 = 1 − ∑ k = 1 n ( X k − X k − 1 ) ( Y k + Y k − 1 ) {\displaystyle G_{1}=1\sum _{k=1}^{n}(X_{k}X_{k1})(Y_{k}+Y_{k1})} ^{[13]}
In this way, it is possible to calculate the AUC by using an average of a number of trapezoidal approximations.
It is also common to calculate the Area Under the ROC Convex Hull (ROC AUCH = ROCH AUC) as any point on the line segment between two prediction results can be achieved by randomly using one or other system with probabilities proportional to the relative length of the opposite component of the segment.^{[14]} Interestingly, it is also possible to invert concavities – just as in the figure the worse solution can be reflected to become a better solution; concavities can be reflected in any line segment, but this more extreme form of fusion is much more likely to overfit the data.^{[15]}
The machine learning community most often uses the ROC AUC statistic for model comparison.^{[16]} However, this practice has recently been questioned based upon new machine learning research that shows that the AUC is quite noisy as a classification measure^{[17]} and has some other significant problems in model comparison.^{[18]}^{[19]} A reliable and valid AUC estimate can be interpreted as the probability that the classifier will assign a higher score to a randomly chosen positive example than to a randomly chosen negative example. However, the critical research^{[17]}^{[18]} suggests frequent failures in obtaining reliable and valid AUC estimates. Thus, the practical value of the AUC measure has been called into question,^{[19]} raising the possibility that the AUC may actually introduce more uncertainty into machine learning classification accuracy comparisons than resolution. Nonetheless, the coherence of AUC as a measure of aggregated classification performance has been vindicated, in terms of a uniform rate distribution,^{[20]} and AUC has been linked to a number of other performance metrics such as the Brier score.^{[21]}
One recent explanation of the problem with ROC AUC is that reducing the ROC Curve to a single number ignores the fact that it is about the tradeoffs between the different systems or performance points plotted and not the performance of an individual system, as well as ignoring the possibility of concavity repair, so that related alternative measures such as Informedness^{[6]} or DeltaP are recommended.^{[22]} These measures are essentially equivalent to the Gini for a single prediction point with DeltaP' = Informedness = 2AUC1, whilst DeltaP = Markedness represents the dual (viz. predicting the prediction from the real class) and their geometric mean is the Matthews correlation coefficient.^{[6]}
Other measures
Whereas ROC AUC varies between 0 and 1 — with an uninformative classifier yielding 0.5 — the alternative measures Informedness^{[6]} and Gini Coefficient (in the single parameterization or single system case)^{[6]} all have the advantage that 0 represents chance performance whilst 1 represents perfect performance, and −1 represents the "perverse" case of full informedness always giving the wrong response.^{[23]} Bringing chance performance to 0 allows these alternative scales to be interpreted as Kappa statistics. Informedness has been shown to have desirable characteristics for Machine Learning versus other common definitions of Kappa such as Cohen Kappa and Fleiss Kappa.^{[6]}^{[24]}
Sometimes it can be more useful to look at a specific region of the ROC Curve rather than at the whole curve. It is possible to compute partial AUC.^{[25]} For example, one could focus on the region of the curve with low false positive rate, which is often of prime interest for population screening tests.^{[26]} Another common approach for classification problems in which P ≪ N (common in bioinformatics applications) is to use a logarithmic scale for the xaxis.^{[27]}
Detection error tradeoff graph
An alternative to the ROC curve is the detection error tradeoff (DET) graph, which plots the false negative rate (missed detections) vs. the false positive rate (false alarms) on nonlinearly transformed x and yaxes. The transformation function is the quantile function of the normal distribution, i.e., the inverse of the cumulative normal distribution. It is, in fact, the same transformation as zROC, below, except that the complement of the hit rate, the miss rate or false negative rate, is used. This alternative spends more graph area on the region of interest. Most of the ROC area is of little interest; one primarily cares about the region tight against the yaxis and the top left corner – which, because of using miss rate instead of its complement, the hit rate, is the lower left corner in a DET plot. Furthermore, DET graphs have the useful property of linearity and a linear threshold behavior for normal distributions.^{[28]} The DET plot is used extensively in the automatic speaker recognition community, where the name DET was first used. The analysis of the ROC performance in graphs with this warping of the axes was used by psychologists in perception studies halfway through the 20th century, where this was dubbed "double probability paper".^{[citation needed]}
Zscore
If a standard score is applied to the ROC curve, the curve will be transformed into a straight line.^{[29]} This zscore is based on a normal distribution with a mean of zero and a standard deviation of one. In memory strength theory, one must assume that the zROC is not only linear, but has a slope of 1.0. The normal distributions of targets (studied objects that the subjects need to recall) and lures (non studied objects that the subjects attempt to recall) is the factor causing the zROC to be linear.
The linearity of the zROC curve depends on the standard deviations of the target and lure strength distributions. If the standard deviations are equal, the slope will be 1.0. If the standard deviation of the target strength distribution is larger than the standard deviation of the lure strength distribution, then the slope will be smaller than 1.0. In most studies, it has been found that the zROC curve slopes constantly fall below 1, usually between 0.5 and 0.9.^{[30]} Many experiments yielded a zROC slope of 0.8. A slope of 0.8 implies that the variability of the target strength distribution is 25% larger than the variability of the lure strength distribution.^{[31]}
Another variable used is d' (d prime) (discussed above in "Other measures"), which can easily be expressed in terms of zvalues. Although d' is a commonly used parameter, it must be recognized that it is only relevant when strictly adhering to the very strong assumptions of strength theory made above.^{[32]}
The zscore of an ROC curve is always linear, as assumed, except in special situations. The Yonelinas familiarityrecollection model is a twodimensional account of recognition memory. Instead of the subject simply answering yes or no to a specific input, the subject gives the input a feeling of familiarity, which operates like the original ROC curve. What changes, though, is a parameter for Recollection (R). Recollection is assumed to be allornone, and it trumps familiarity. If there were no recollection component, zROC would have a predicted slope of 1. However, when adding the recollection component, the zROC curve will be concave up, with a decreased slope. This difference in shape and slope result from an added element of variability due to some items being recollected. Patients with anterograde amnesia are unable to recollect, so their Yonelinas zROC curve would have a slope close to 1.0.^{[33]}
History
The ROC curve was first used during World War II for the analysis of radar signals before it was employed in signal detection theory.^{[34]} Following the attack on Pearl Harbor in 1941, the United States army began new research to increase the prediction of correctly detected Japanese aircraft from their radar signals. For this purposes they measured the ability of radar receiver operators to make these important distinctions, which was called the Receiver Operating Characteristics.^{[35]}
In the 1950s, ROC curves were employed in psychophysics to assess human (and occasionally nonhuman animal) detection of weak signals.^{[34]} In medicine, ROC analysis has been extensively used in the evaluation of diagnostic tests.^{[36]}^{[37]} ROC curves are also used extensively in epidemiology and medical research and are frequently mentioned in conjunction with evidencebased medicine. In radiology, ROC analysis is a common technique to evaluate new radiology techniques.^{[38]} In the social sciences, ROC analysis is often called the ROC Accuracy Ratio, a common technique for judging the accuracy of default probability models. ROC curves are widely used in laboratory medicine to assess diagnostic accuracy of a test, to choose the optimal cutoff of a test and to compare diagnostic accuracy of several tests.
ROC curves also proved useful for the evaluation of machine learning techniques. The first application of ROC in machine learning was by Spackman who demonstrated the value of ROC curves in comparing and evaluating different classification algorithms.^{[39]}
ROC curves beyond binary classification
The extension of ROC curves for classification problems with more than two classes has always been cumbersome, as the degrees of freedom increase quadratically with the number of classes, and the ROC space has $c(c1)$ dimensions, where $c$ is the number of classes.^{[40]} Some approaches have been made for the particular case with three classes (threeway ROC).^{[41]} The calculation of the volume under the ROC surface (VUS) has been analyzed and studied as a performance metric for multiclass problems.^{[42]} However, because of the complexity of approximating the true VUS, some other approaches ^{[43]} based on an extension of AUC are more popular as an evaluation metric.
Given the success of ROC curves for the assessment of classification models, the extension of ROC curves for other supervised tasks has also been investigated. Notable proposals for regression problems are the socalled regression error characteristic (REC) Curves ^{[44]} and the Regression ROC (RROC) curves.^{[45]} In the latter, RROC curves become extremely similar to ROC curves for classification, with the notions of asymmetry, dominance and convex hull. Also, the area under RROC curves is proportional to the error variance of the regression model.
See also
Sources: Fawcett (2006), Powers (2011), and Ting (2011) ^{[46]} ^{[47]} ^{[48]} 
 Statistics portal
Wikimedia Commons has media related to Receiver operating characteristic. 
 Brier score
 Coefficient of determination
 Constant false alarm rate
 Detection error tradeoff
 Detection theory
 F1 score
 False alarm
 Precision and recall
 ROCCET
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 ^ Ratcliff, Roger; McCoon, Gail; Tindall, Michael (1994). "Empirical generality of data from recognition memory ROC functions and implications for GMMs". Journal of Experimental Psychology: Learning, Memory, and Cognition. 20: 763–785. doi:10.1037/02787393.20.4.763.
 ^ Zhang, Jun; Mueller, Shane T. (2005). "A note on ROC analysis and nonparametric estimate of sensitivity". Psychometrika. 70: 203–212. doi:10.1007/s1133600311198.
 ^ Yonelinas, Andrew P.; Kroll, Neal E. A.; Dobbins, Ian G.; Lazzara, Michele; Knight, Robert T. (1998). "Recollection and familiarity deficits in amnesia: Convergence of rememberknow, process dissociation, and receiver operating characteristic data". Neuropsychology. 12: 323–339. doi:10.1037/08944105.12.3.323.
 ^ ^{a} ^{b} Green, David M.; Swets, John A. (1966). Signal detection theory and psychophysics. New York, NY: John Wiley and Sons Inc. ISBN 0471324205.
 ^ "Using the Receiver Operating Characteristic (ROC) curve to analyze a classification model: A final note of historical interest" (PDF). Department of Mathematics, University of Utah. Department of Mathematics, University of Utah. Retrieved May 25, 2017.
 ^ Zweig, Mark H.; Campbell, Gregory (1993). "Receiveroperating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine" (PDF). Clinical Chemistry. 39 (8): 561–577. PMID 8472349.
 ^ Pepe, Margaret S. (2003). The statistical evaluation of medical tests for classification and prediction. New York, NY: Oxford. ISBN 0198565828.
 ^ Obuchowski, Nancy A. (2003). "Receiver operating characteristic curves and their use in radiology". Radiology. 229 (1): 3–8. doi:10.1148/radiol.2291010898. PMID 14519861.
 ^ Spackman, Kent A. (1989). "Signal detection theory: Valuable tools for evaluating inductive learning". Proceedings of the Sixth International Workshop on Machine Learning. San Mateo, CA: Morgan Kaufmann. pp. 160–163.
 ^ Srinivasan, A. (1999). "Note on the Location of Optimal Classifiers in Ndimensional ROC Space". Technical Report PRGTR299, Oxford University Computing Laboratory, Wolfson Building, Parks Road, Oxford.
 ^ Mossman, D. (1999). "Threeway ROCs". Medical Decision Making. 19: 78–89. doi:10.1177/0272989x9901900110.
 ^ Ferri, C.; HernandezOrallo, J.; Salido, M.A. (2003). "Volume under the ROC Surface for Multiclass Problems". Machine Learning: ECML 2003. pp. 108–120.
 ^ Till, D.J.; Hand, R.J. (2012). "A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems". Machine Learning. 45: 171–186. doi:10.1023/A:1010920819831.
 ^ Bi, J.; Bennett, K.P. (2003). "Regression error characteristic curves". Twentieth International Conference on Machine Learning (ICML2003). Washington, DC.
 ^ HernandezOrallo, J. (2013). "ROC curves for regression". Pattern Recognition. 46 (12): 3395–3411 . doi:10.1016/j.patcog.2013.06.014.
 ^ Fawcett, Tom (2006). "An Introduction to ROC Analysis" (PDF). Pattern Recognition Letters. 27 (8): 861–874. doi:10.1016/j.patrec.2005.10.010.
 ^ Powers, David M W (2011). "Evaluation: From Precision, Recall and FMeasure to ROC, Informedness, Markedness & Correlation" (PDF). Journal of Machine Learning Technologies. 2 (1): 37–63.
 ^ Ting, Kai Ming (2011). Encyclopedia of machine learning. Springer. ISBN 9780387301648.
External links
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Further reading
 Balakrishnan, Narayanaswamy (1991); Handbook of the Logistic Distribution, Marcel Dekker, Inc., ISBN 9780824785871
 Brown, Christopher D.; Davis, Herbert T. (2006). "Receiver operating characteristic curves and related decision measures: a tutorial". Chemometrics and Intelligent Laboratory Systems. 80: 24–38. doi:10.1016/j.chemolab.2005.05.004.
 Rotello, Caren M.; Heit, Evan; Dubé, Chad (2014). "When more data steer us wrong: replications with the wrong dependent measure perpetuate erroneous conclusions" (PDF). Psychonomic Bulletin & Review. 22: 944–954. doi:10.3758/s1342301407592.
 Fawcett, Tom (2004). "ROC Graphs: Notes and Practical Considerations for Researchers" (PDF). Pattern Recognition Letters. 27 (8): 882–891.
 Gonen, Mithat (2007); Analyzing Receiver Operating Characteristic Curves Using SAS, SAS Press, ISBN 9781599942988
 Green, William H., (2003) Econometric Analysis, fifth edition, Prentice Hall, ISBN 0130661899
 Heagerty, Patrick J.; Lumley, Thomas; Pepe, Margaret S. (2000). "Timedependent ROC Curves for Censored Survival Data and a Diagnostic Marker". Biometrics. 56: 337–344. doi:10.1111/j.0006341x.2000.00337.x.
 Hosmer, David W.; and Lemeshow, Stanley (2000); Applied Logistic Regression, 2nd ed., New York, NY: Wiley, ISBN 0471356328
 Lasko, Thomas A.; Bhagwat, Jui G.; Zou, Kelly H.; OhnoMachado, Lucila (2005). "The use of receiver operating characteristic curves in biomedical informatics". Journal of Biomedical Informatics. 38 (5): 404–415. doi:10.1016/j.jbi.2005.02.008.
 Stephan, Carsten; Wesseling, Sebastian; Schink, Tania; Jung, Klaus (2003). "Comparison of Eight Computer Programs for ReceiverOperating Characteristic Analysis". Clinical Chemistry. 49: 433–439. doi:10.1373/49.3.433.
 Swets, John A.; Dawes, Robyn M.; and Monahan, John (2000); Better Decisions through Science, Scientific American, October, pp. 82–87
 Zou, Kelly H.; O'Malley, A. James; Mauri, Laura (2007). "Receiveroperating characteristic analysis for evaluating diagnostic tests and predictive models". Circulation. 115 (5): 654–7. doi:10.1161/circulationaha.105.594929.
 Zhou, XiaoHua; Obuchowski, Nancy A.; McClish, Donna K. (2002). Statistical Methods in Diagnostic Medicine. New York, NY: Wiley & Sons. ISBN 9780471347729.
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出典(authority):フリー百科事典『ウィキペディア（Wikipedia）』「2018/02/24 19:03:51」(JST)
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[Wiki en表示]In statistics, a receiver operating characteristic curve, i.e. ROC curve, is a graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied.
The ROC curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings. The truepositive rate is also known as sensitivity, recall or probability of detection^{[1]} in machine learning. The falsepositive rate is also known as the fallout or probability of false alarm^{[1]} and can be calculated as (1 − specificity). It can also be thought of as a plot of the Power as a function of the Type I Error of the decision rule (when the performance is calculated from just a sample of the population, it can be thought of as estimators of these quantities). The ROC curve is thus the sensitivity as a function of fallout. In general, if the probability distributions for both detection and false alarm are known, the ROC curve can be generated by plotting the cumulative distribution function (area under the probability distribution from $\infty$ to the discrimination threshold) of the detection probability in the yaxis versus the cumulative distribution function of the falsealarm probability on the xaxis.
ROC analysis provides tools to select possibly optimal models and to discard suboptimal ones independently from (and prior to specifying) the cost context or the class distribution. ROC analysis is related in a direct and natural way to cost/benefit analysis of diagnostic decision making.
The ROC curve was first developed by electrical engineers and radar engineers during World War II for detecting enemy objects in battlefields and was soon introduced to psychology to account for perceptual detection of stimuli. ROC analysis since then has been used in medicine, radiology, biometrics, forecasting of natural hazards,^{[2]} meteorology,^{[3]} model performance assessment,^{[4]} and other areas for many decades and is increasingly used in machine learning and data mining research.
The ROC is also known as a relative operating characteristic curve, because it is a comparison of two operating characteristics (TPR and FPR) as the criterion changes.^{[5]}
Contents
 1 Basic concept
 2 ROC space
 3 Curves in ROC space
 4 Further interpretations
 4.1 Area under the curve
 4.2 Other measures
 5 Detection error tradeoff graph
 6 Zscore
 7 History
 8 ROC curves beyond binary classification
 9 See also
 10 References
 11 External links
 12 Further reading
Basic concept
A classification model (classifier or diagnosis) is a mapping of instances between certain classes/groups. The classifier or diagnosis result can be a real value (continuous output), in which case the classifier boundary between classes must be determined by a threshold value (for instance, to determine whether a person has hypertension based on a blood pressure measure). Or it can be a discrete class label, indicating one of the classes.
Let us consider a twoclass prediction problem (binary classification), in which the outcomes are labeled either as positive (p) or negative (n). There are four possible outcomes from a binary classifier. If the outcome from a prediction is p and the actual value is also p, then it is called a true positive (TP); however if the actual value is n then it is said to be a false positive (FP). Conversely, a true negative (TN) has occurred when both the prediction outcome and the actual value are n, and false negative (FN) is when the prediction outcome is n while the actual value is p.
To get an appropriate example in a realworld problem, consider a diagnostic test that seeks to determine whether a person has a certain disease. A false positive in this case occurs when the person tests positive, but does not actually have the disease. A false negative, on the other hand, occurs when the person tests negative, suggesting they are healthy, when they actually do have the disease.
Let us define an experiment from P positive instances and N negative instances for some condition. The four outcomes can be formulated in a 2×2 contingency table or confusion matrix, as follows:
True condition  
Total population  Condition positive  Condition negative  Prevalence = Σ Condition positive/Σ Total population  Accuracy (ACC) = Σ True positive + Σ True negative/Σ Total population  
Predicted condition 
Predicted condition positive 
True positive, Power 
False positive, Type I error 
Positive predictive value (PPV), Precision = Σ True positive/Σ Predicted condition positive  False discovery rate (FDR) = Σ False positive/Σ Predicted condition positive  
Predicted condition negative 
False negative, Type II error 
True negative  False omission rate (FOR) = Σ False negative/Σ Predicted condition negative  Negative predictive value (NPV) = Σ True negative/Σ Predicted condition negative  
True positive rate (TPR), Recall, Sensitivity, probability of detection = Σ True positive/Σ Condition positive  False positive rate (FPR), Fallout, probability of false alarm = Σ False positive/Σ Condition negative  Positive likelihood ratio (LR+) = TPR/FPR  Diagnostic odds ratio (DOR) = LR+/LR−  F_{1} score = 2/1/Recall + 1/Precision  
False negative rate (FNR), Miss rate = Σ False negative/Σ Condition positive  True negative rate (TNR), Specificity (SPC) = Σ True negative/Σ Condition negative  Negative likelihood ratio (LR−) = FNR/TNR 
ROC space
The contingency table can derive several evaluation "metrics" (see infobox). To draw a ROC curve, only the true positive rate (TPR) and false positive rate (FPR) are needed (as functions of some classifier parameter). The TPR defines how many correct positive results occur among all positive samples available during the test. FPR, on the other hand, defines how many incorrect positive results occur among all negative samples available during the test.
A ROC space is defined by FPR and TPR as x and y axes, respectively, which depicts relative tradeoffs between true positive (benefits) and false positive (costs). Since TPR is equivalent to sensitivity and FPR is equal to 1 − specificity, the ROC graph is sometimes called the sensitivity vs (1 − specificity) plot. Each prediction result or instance of a confusion matrix represents one point in the ROC space.
The best possible prediction method would yield a point in the upper left corner or coordinate (0,1) of the ROC space, representing 100% sensitivity (no false negatives) and 100% specificity (no false positives). The (0,1) point is also called a perfect classification. A random guess would give a point along a diagonal line (the socalled line of nodiscrimination) from the left bottom to the top right corners (regardless of the positive and negative base rates). An intuitive example of random guessing is a decision by flipping coins. As the size of the sample increases, a random classifier's ROC point migrates towards the diagonal line. In the case of a balanced coin, it will migrate to the point (0.5, 0.5).
The diagonal divides the ROC space. Points above the diagonal represent good classification results (better than random), points below the line represent poor results (worse than random). Note that the output of a consistently poor predictor could simply be inverted to obtain a good predictor.
Let us look into four prediction results from 100 positive and 100 negative instances (please keep in mind that the table layout is flipped compared to the table above):
A  B  C  C′  






TPR = 0.63  TPR = 0.77  TPR = 0.24  TPR = 0.76  
FPR = 0.28  FPR = 0.77  FPR = 0.88  FPR = 0.12  
PPV = 0.69  PPV = 0.50  PPV = 0.21  PPV = 0.86  
F1 = 0.66  F1 = 0.61  F1 = 0.22  F1 = 0.81  
ACC = 0.68  ACC = 0.50  ACC = 0.18  ACC = 0.82 
Plots of the four results above in the ROC space are given in the figure. The result of method A clearly shows the best predictive power among A, B, and C. The result of B lies on the random guess line (the diagonal line), and it can be seen in the table that the accuracy of B is 50%. However, when C is mirrored across the center point (0.5,0.5), the resulting method C′ is even better than A. This mirrored method simply reverses the predictions of whatever method or test produced the C contingency table. Although the original C method has negative predictive power, simply reversing its decisions leads to a new predictive method C′ which has positive predictive power. When the C method predicts p or n, the C′ method would predict n or p, respectively. In this manner, the C′ test would perform the best. The closer a result from a contingency table is to the upper left corner, the better it predicts, but the distance from the random guess line in either direction is the best indicator of how much predictive power a method has. If the result is below the line (i.e. the method is worse than a random guess), all of the method's predictions must be reversed in order to utilize its power, thereby moving the result above the random guess line.
Curves in ROC space
In binary classification, the class prediction for each instance is often made based on a continuous random variable $X$, which is a "score" computed for the instance (e.g. estimated probability in logistic regression). Given a threshold parameter $T$, the instance is classified as "positive" if $X>T$, and "negative" otherwise. $X$ follows a probability density $f_{1}(x)$ if the instance actually belongs to class "positive", and $f_{0}(x)$ if otherwise. Therefore, the true positive rate is given by ${\mbox{TPR}}(T)=\int _{T}^{\infty }f_{1}(x)\,dx$ and the false positive rate is given by ${\mbox{FPR}}(T)=\int _{T}^{\infty }f_{0}(x)\,dx$. The ROC curve plots parametrically TPR(T) versus FPR(T) with T as the varying parameter.
For example, imagine that the blood protein levels in diseased people and healthy people are normally distributed with means of 2 g/dL and 1 g/dL respectively. A medical test might measure the level of a certain protein in a blood sample and classify any number above a certain threshold as indicating disease. The experimenter can adjust the threshold (black vertical line in the figure), which will in turn change the false positive rate. Increasing the threshold would result in fewer false positives (and more false negatives), corresponding to a leftward movement on the curve. The actual shape of the curve is determined by how much overlap the two distributions have. These concepts are demonstrated in the Receiver Operating Characteristic (ROC) Curves Applet.
Further interpretations
Sometimes, the ROC is used to generate a summary statistic. Common versions are:
 the intercept of the ROC curve with the line at 45 degrees orthogonal to the nodiscrimination line  the balance point where Sensitivity = Specificity
 the intercept of the ROC curve with the tangent at 45 degrees parallel to the nodiscrimination line that is closest to the errorfree point (0,1)  also called Youden's J statistic and generalized as Informedness^{[6]}
 the area between the ROC curve and the nodiscrimination line  Gini Coefficient
 the area between the full ROC curve and the triangular ROC curve including only (0,0), (1,1) and one selected operating point (tpr,fpr)  Consistency^{[7]}
 the area under the ROC curve, or "AUC" ("Area Under Curve"), or A' (pronounced "aprime"),^{[8]} or "cstatistic".^{[9]}
 the sensitivity index d' (pronounced "dprime"), the distance between the mean of the distribution of activity in the system under noisealone conditions and its distribution under signalalone conditions, divided by their standard deviation, under the assumption that both these distributions are normal with the same standard deviation. Under these assumptions, the shape of the ROC is entirely determined by d'.
However, any attempt to summarize the ROC curve into a single number loses information about the pattern of tradeoffs of the particular discriminator algorithm.
Area under the curve
When using normalized units, the area under the curve (often referred to as simply the AUC) 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').^{[10]} This can be seen as follows: the area under the curve is given by (the integral boundaries are reversed as large T has a lower value on the xaxis)
 $$
A = ∫ ∞ − ∞ TPR ( T ) ( − FPR ′ ( T ) ) d T = ∫ − ∞ ∞ ∫ − ∞ ∞ I ( T ′ > T ) f 1 ( T ′ ) f 0 ( T ) d T ′ d T = P ( X 1 > X 0 ) {\displaystyle A=\int _{\infty }^{\infty }{\mbox{TPR}}(T)\left({\mbox{FPR}}'(T)\right)\,dT=\int _{\infty }^{\infty }\int _{\infty }^{\infty }I(T'>T)f_{1}(T')f_{0}(T)\,dT'\,dT=P(X_{1}>X_{0})}
where $X_{1}$ is the score for a positive instance and $X_{0}$ is the score for a negative instance, and $f_{0}$ and $f_{1}$ are probability densities as defined in previous section.
It can further be shown that the AUC is closely related to the Mann–Whitney U,^{[11]}^{[12]} which tests whether positives are ranked higher than negatives. It is also equivalent to the Wilcoxon test of ranks.^{[12]} The AUC is related to the Gini coefficient ($G_{1}$) by the formula $G_{1}=2{\mbox{AUC}}1$, where:
 $$
G 1 = 1 − ∑ k = 1 n ( X k − X k − 1 ) ( Y k + Y k − 1 ) {\displaystyle G_{1}=1\sum _{k=1}^{n}(X_{k}X_{k1})(Y_{k}+Y_{k1})} ^{[13]}
In this way, it is possible to calculate the AUC by using an average of a number of trapezoidal approximations.
It is also common to calculate the Area Under the ROC Convex Hull (ROC AUCH = ROCH AUC) as any point on the line segment between two prediction results can be achieved by randomly using one or other system with probabilities proportional to the relative length of the opposite component of the segment.^{[14]} Interestingly, it is also possible to invert concavities – just as in the figure the worse solution can be reflected to become a better solution; concavities can be reflected in any line segment, but this more extreme form of fusion is much more likely to overfit the data.^{[15]}
The machine learning community most often uses the ROC AUC statistic for model comparison.^{[16]} However, this practice has recently been questioned based upon new machine learning research that shows that the AUC is quite noisy as a classification measure^{[17]} and has some other significant problems in model comparison.^{[18]}^{[19]} A reliable and valid AUC estimate can be interpreted as the probability that the classifier will assign a higher score to a randomly chosen positive example than to a randomly chosen negative example. However, the critical research^{[17]}^{[18]} suggests frequent failures in obtaining reliable and valid AUC estimates. Thus, the practical value of the AUC measure has been called into question,^{[19]} raising the possibility that the AUC may actually introduce more uncertainty into machine learning classification accuracy comparisons than resolution. Nonetheless, the coherence of AUC as a measure of aggregated classification performance has been vindicated, in terms of a uniform rate distribution,^{[20]} and AUC has been linked to a number of other performance metrics such as the Brier score.^{[21]}
One recent explanation of the problem with ROC AUC is that reducing the ROC Curve to a single number ignores the fact that it is about the tradeoffs between the different systems or performance points plotted and not the performance of an individual system, as well as ignoring the possibility of concavity repair, so that related alternative measures such as Informedness^{[6]} or DeltaP are recommended.^{[22]} These measures are essentially equivalent to the Gini for a single prediction point with DeltaP' = Informedness = 2AUC1, whilst DeltaP = Markedness represents the dual (viz. predicting the prediction from the real class) and their geometric mean is the Matthews correlation coefficient.^{[6]}
Other measures
Whereas ROC AUC varies between 0 and 1 — with an uninformative classifier yielding 0.5 — the alternative measures Informedness^{[6]} and Gini Coefficient (in the single parameterization or single system case)^{[6]} all have the advantage that 0 represents chance performance whilst 1 represents perfect performance, and −1 represents the "perverse" case of full informedness always giving the wrong response.^{[23]} Bringing chance performance to 0 allows these alternative scales to be interpreted as Kappa statistics. Informedness has been shown to have desirable characteristics for Machine Learning versus other common definitions of Kappa such as Cohen Kappa and Fleiss Kappa.^{[6]}^{[24]}
Sometimes it can be more useful to look at a specific region of the ROC Curve rather than at the whole curve. It is possible to compute partial AUC.^{[25]} For example, one could focus on the region of the curve with low false positive rate, which is often of prime interest for population screening tests.^{[26]} Another common approach for classification problems in which P ≪ N (common in bioinformatics applications) is to use a logarithmic scale for the xaxis.^{[27]}
Detection error tradeoff graph
An alternative to the ROC curve is the detection error tradeoff (DET) graph, which plots the false negative rate (missed detections) vs. the false positive rate (false alarms) on nonlinearly transformed x and yaxes. The transformation function is the quantile function of the normal distribution, i.e., the inverse of the cumulative normal distribution. It is, in fact, the same transformation as zROC, below, except that the complement of the hit rate, the miss rate or false negative rate, is used. This alternative spends more graph area on the region of interest. Most of the ROC area is of little interest; one primarily cares about the region tight against the yaxis and the top left corner – which, because of using miss rate instead of its complement, the hit rate, is the lower left corner in a DET plot. Furthermore, DET graphs have the useful property of linearity and a linear threshold behavior for normal distributions.^{[28]} The DET plot is used extensively in the automatic speaker recognition community, where the name DET was first used. The analysis of the ROC performance in graphs with this warping of the axes was used by psychologists in perception studies halfway through the 20th century, where this was dubbed "double probability paper".^{[citation needed]}
Zscore
If a standard score is applied to the ROC curve, the curve will be transformed into a straight line.^{[29]} This zscore is based on a normal distribution with a mean of zero and a standard deviation of one. In memory strength theory, one must assume that the zROC is not only linear, but has a slope of 1.0. The normal distributions of targets (studied objects that the subjects need to recall) and lures (non studied objects that the subjects attempt to recall) is the factor causing the zROC to be linear.
The linearity of the zROC curve depends on the standard deviations of the target and lure strength distributions. If the standard deviations are equal, the slope will be 1.0. If the standard deviation of the target strength distribution is larger than the standard deviation of the lure strength distribution, then the slope will be smaller than 1.0. In most studies, it has been found that the zROC curve slopes constantly fall below 1, usually between 0.5 and 0.9.^{[30]} Many experiments yielded a zROC slope of 0.8. A slope of 0.8 implies that the variability of the target strength distribution is 25% larger than the variability of the lure strength distribution.^{[31]}
Another variable used is d' (d prime) (discussed above in "Other measures"), which can easily be expressed in terms of zvalues. Although d' is a commonly used parameter, it must be recognized that it is only relevant when strictly adhering to the very strong assumptions of strength theory made above.^{[32]}
The zscore of an ROC curve is always linear, as assumed, except in special situations. The Yonelinas familiarityrecollection model is a twodimensional account of recognition memory. Instead of the subject simply answering yes or no to a specific input, the subject gives the input a feeling of familiarity, which operates like the original ROC curve. What changes, though, is a parameter for Recollection (R). Recollection is assumed to be allornone, and it trumps familiarity. If there were no recollection component, zROC would have a predicted slope of 1. However, when adding the recollection component, the zROC curve will be concave up, with a decreased slope. This difference in shape and slope result from an added element of variability due to some items being recollected. Patients with anterograde amnesia are unable to recollect, so their Yonelinas zROC curve would have a slope close to 1.0.^{[33]}
History
The ROC curve was first used during World War II for the analysis of radar signals before it was employed in signal detection theory.^{[34]} Following the attack on Pearl Harbor in 1941, the United States army began new research to increase the prediction of correctly detected Japanese aircraft from their radar signals. For this purposes they measured the ability of radar receiver operators to make these important distinctions, which was called the Receiver Operating Characteristics.^{[35]}
In the 1950s, ROC curves were employed in psychophysics to assess human (and occasionally nonhuman animal) detection of weak signals.^{[34]} In medicine, ROC analysis has been extensively used in the evaluation of diagnostic tests.^{[36]}^{[37]} ROC curves are also used extensively in epidemiology and medical research and are frequently mentioned in conjunction with evidencebased medicine. In radiology, ROC analysis is a common technique to evaluate new radiology techniques.^{[38]} In the social sciences, ROC analysis is often called the ROC Accuracy Ratio, a common technique for judging the accuracy of default probability models. ROC curves are widely used in laboratory medicine to assess diagnostic accuracy of a test, to choose the optimal cutoff of a test and to compare diagnostic accuracy of several tests.
ROC curves also proved useful for the evaluation of machine learning techniques. The first application of ROC in machine learning was by Spackman who demonstrated the value of ROC curves in comparing and evaluating different classification algorithms.^{[39]}
ROC curves are also used in verification of forecasts in meteorology.^{[40]}
ROC curves beyond binary classification
The extension of ROC curves for classification problems with more than two classes has always been cumbersome, as the degrees of freedom increase quadratically with the number of classes, and the ROC space has $c(c1)$ dimensions, where $c$ is the number of classes.^{[41]} Some approaches have been made for the particular case with three classes (threeway ROC).^{[42]} The calculation of the volume under the ROC surface (VUS) has been analyzed and studied as a performance metric for multiclass problems.^{[43]} However, because of the complexity of approximating the true VUS, some other approaches ^{[44]} based on an extension of AUC are more popular as an evaluation metric.
Given the success of ROC curves for the assessment of classification models, the extension of ROC curves for other supervised tasks has also been investigated. Notable proposals for regression problems are the socalled regression error characteristic (REC) Curves ^{[45]} and the Regression ROC (RROC) curves.^{[46]} In the latter, RROC curves become extremely similar to ROC curves for classification, with the notions of asymmetry, dominance and convex hull. Also, the area under RROC curves is proportional to the error variance of the regression model.
See also
Sources: Fawcett (2006), Powers (2011), and Ting (2011) ^{[47]} ^{[48]} ^{[49]} 
 Statistics portal
Wikimedia Commons has media related to Receiver operating characteristic. 
 Brier score
 Coefficient of determination
 Constant false alarm rate
 Detection error tradeoff
 Detection theory
 F1 score
 False alarm
 Precision and recall
 ROCCET
References
 ^ ^{a} ^{b} "Detector Performance Analysis Using ROC Curves  MATLAB & Simulink Example". www.mathworks.com. Retrieved 11 August 2016.
 ^ Peres, D. J.; Cancelliere, A. (20141208). "Derivation and evaluation of landslidetriggering thresholds by a Monte Carlo approach". Hydrol. Earth Syst. Sci. 18 (12): 4913–4931. doi:10.5194/hess1849132014. ISSN 16077938.
 ^ Murphy, Allan H. (19960301). "The Finley Affair: A Signal Event in the History of Forecast Verification". Weather and Forecasting. 11 (1): 3–20. doi:10.1175/15200434(1996)011<0003:tfaase>2.0.co;2. ISSN 08828156.
 ^ Peres, D. J.; Iuppa, C.; Cavallaro, L.; Cancelliere, A.; Foti, E. (20151001). "Significant wave height record extension by neural networks and reanalysis wind data". Ocean Modelling. 94: 128–140. doi:10.1016/j.ocemod.2015.08.002.
 ^ Swets, John A.; Signal detection theory and ROC analysis in psychology and diagnostics : collected papers, Lawrence Erlbaum Associates, Mahwah, NJ, 1996
 ^ ^{a} ^{b} ^{c} ^{d} ^{e} ^{f} Powers, David M W (2011) [2007]. "Evaluation: From Precision, Recall and FMeasure to ROC, Informedness, Markedness & Correlation" (PDF). Journal of Machine Learning Technologies. 2 (1): 37–63. [1]
 ^ Powers, David MW (2012). "ROCConCert: ROCBased Measurement of Consistency and Certainty". Spring Congress on Engineering and Technology (SCET). 2. IEEE. pp. 238–241.
 ^ Fogarty, James; Baker, Ryan S.; Hudson, Scott E. (2005). "Case studies in the use of ROC curve analysis for sensorbased estimates in human computer interaction". ACM International Conference Proceeding Series, Proceedings of Graphics Interface 2005. Waterloo, ON: Canadian HumanComputer Communications Society.
 ^ Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome H. (2009). The elements of statistical learning: data mining, inference, and prediction (2nd ed.).
 ^ Fawcett, Tom (2006); An introduction to ROC analysis, Pattern Recognition Letters, 27, 861–874.
 ^ Hanley, James A.; McNeil, Barbara J. (1982). "The Meaning and Use of the Area under a Receiver Operating Characteristic (ROC) Curve". Radiology. 143 (1): 29–36. doi:10.1148/radiology.143.1.7063747. PMID 7063747.
 ^ ^{a} ^{b} Mason, Simon J.; Graham, Nicholas E. (2002). "Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves: Statistical significance and interpretation" (PDF). Quarterly Journal of the Royal Meteorological Society. 128: 2145–2166. doi:10.1256/003590002320603584.
 ^ Hand, David J.; and Till, Robert J. (2001); A simple generalization of the area under the ROC curve for multiple class classification problems, Machine Learning, 45, 171–186.
 ^ Provost, F.; Fawcett, T. (2001). "Robust classification for imprecise environments". Machine Learning. 42: 203–231. doi:10.1023/a:1007601015854.
 ^ Flach, P.A.; Wu, S. (2005). "Repairing concavities in ROC curves." (PDF). 19th International Joint Conference on Artificial Intelligence (IJCAI'05). pp. 702–707.
 ^ Hanley, James A.; McNeil, Barbara J. (19830901). "A method of comparing the areas under receiver operating characteristic curves derived from the same cases". Radiology. 148 (3): 839–843. doi:10.1148/radiology.148.3.6878708. PMID 6878708. Retrieved 20081203.
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 ^ Ratcliff, Roger; McCoon, Gail; Tindall, Michael (1994). "Empirical generality of data from recognition memory ROC functions and implications for GMMs". Journal of Experimental Psychology: Learning, Memory, and Cognition. 20: 763–785. doi:10.1037/02787393.20.4.763.
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 ^ Yonelinas, Andrew P.; Kroll, Neal E. A.; Dobbins, Ian G.; Lazzara, Michele; Knight, Robert T. (1998). "Recollection and familiarity deficits in amnesia: Convergence of rememberknow, process dissociation, and receiver operating characteristic data". Neuropsychology. 12: 323–339. doi:10.1037/08944105.12.3.323.
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 ^ Ferri, C.; HernandezOrallo, J.; Salido, M.A. (2003). "Volume under the ROC Surface for Multiclass Problems". Machine Learning: ECML 2003. pp. 108–120.
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 ^ Ting, Kai Ming (2011). Encyclopedia of machine learning. Springer. ISBN 9780387301648.
External links
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Further reading
 Balakrishnan, Narayanaswamy (1991); Handbook of the Logistic Distribution, Marcel Dekker, Inc., ISBN 9780824785871
 Brown, Christopher D.; Davis, Herbert T. (2006). "Receiver operating characteristic curves and related decision measures: a tutorial". Chemometrics and Intelligent Laboratory Systems. 80: 24–38. doi:10.1016/j.chemolab.2005.05.004.
 Rotello, Caren M.; Heit, Evan; Dubé, Chad (2014). "When more data steer us wrong: replications with the wrong dependent measure perpetuate erroneous conclusions" (PDF). Psychonomic Bulletin & Review. 22: 944–954. doi:10.3758/s1342301407592.
 Fawcett, Tom (2004). "ROC Graphs: Notes and Practical Considerations for Researchers" (PDF). Pattern Recognition Letters. 27 (8): 882–891.
 Gonen, Mithat (2007); Analyzing Receiver Operating Characteristic Curves Using SAS, SAS Press, ISBN 9781599942988
 Green, William H., (2003) Econometric Analysis, fifth edition, Prentice Hall, ISBN 0130661899
 Heagerty, Patrick J.; Lumley, Thomas; Pepe, Margaret S. (2000). "Timedependent ROC Curves for Censored Survival Data and a Diagnostic Marker". Biometrics. 56: 337–344. doi:10.1111/j.0006341x.2000.00337.x.
 Hosmer, David W.; and Lemeshow, Stanley (2000); Applied Logistic Regression, 2nd ed., New York, NY: Wiley, ISBN 0471356328
 Lasko, Thomas A.; Bhagwat, Jui G.; Zou, Kelly H.; OhnoMachado, Lucila (2005). "The use of receiver operating characteristic curves in biomedical informatics". Journal of Biomedical Informatics. 38 (5): 404–415. doi:10.1016/j.jbi.2005.02.008.
 Stephan, Carsten; Wesseling, Sebastian; Schink, Tania; Jung, Klaus (2003). "Comparison of Eight Computer Programs for ReceiverOperating Characteristic Analysis". Clinical Chemistry. 49: 433–439. doi:10.1373/49.3.433.
 Swets, John A.; Dawes, Robyn M.; and Monahan, John (2000); Better Decisions through Science, Scientific American, October, pp. 82–87
 Zou, Kelly H.; O'Malley, A. James; Mauri, Laura (2007). "Receiveroperating characteristic analysis for evaluating diagnostic tests and predictive models". Circulation. 115 (5): 654–7. doi:10.1161/circulationaha.105.594929.
 Zhou, XiaoHua; Obuchowski, Nancy A.; McClish, Donna K. (2002). Statistical Methods in Diagnostic Medicine. New York, NY: Wiley & Sons. ISBN 9780471347729.
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UpToDate Contents
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 1. 診断的検査の評価 evaluating diagnostic tests
 2. 生物統計学および疫学に関する一般用語集 glossary of common biostatistical and epidemiological terms
 3. Model for Endstage Liver Disease (MELD)
 4. 遺伝関連研究：原理および応用 genetic association studies principles and applications
 5. 無症候性左室機能不全に対するスクリーニング screening for asymptomatic left ventricular dysfunction
英文文献
 EVALUATION OF DIAGNOSTIC POTENTIAL OF Rv3803c AND Rv2626c RECOMBINANT ANTIGENS IN TB ENDEMIC COUNTRY PAKISTAN.
 Ashraf S, Saqib MA, Sharif MZ, Khatak AA, Khan SN, Malik SA, Tahseen S, Khanum A.Author information a Pakistan Medical Research Council , Faisalabad , Pakistan.AbstractTo overcome and eliminate tuberculosis (TB), definitive, reliable, and rapid diagnosis is mandatory. Presently, the diagnostic potential of acute and latent stage TB specific antigens i.e., Rv3803c and Rv2626c was determined. Immunogenic recombinant genes of Rv3803c and Rv2626c antigens were cloned in bacterial expression vector pET23b and expressed product was purified. The homogeneity and structural integrity was confirmed by Western blot analysis. Diagnostic potential of Rv3803c and Rv2626c antigens was analyzed using the sera of 140 active TB patients (AFB smear positive) by indirect ELISA. Ten patients of leprosy and 94 healthy individuals were taken as disease and normal control respectively. The data was analyzed using R statistical package. The sensitivity and specificity of Rv3803c in active TB patients was of 69.3% and 76.4% respectively with an area under ROC curve of 0.77, whereas sensitivity and specificity of Rv2626c 77.1% and 85.1%, respectively. The area under ROC curve of Rv2626c was 0.89 which was significantly higher than Rv3803c (p < 0.0001). Recombinant antigens Rv3803c and Rv2626c have potential to be used as diagnostic markers for TB and need to evaluate with other antigens for differential diagnosis of TB.
 Journal of immunoassay & immunochemistry.J Immunoassay Immunochem.2014 Apr 3;35(2):1209. doi: 10.1080/15321819.2013.824897.
 To overcome and eliminate tuberculosis (TB), definitive, reliable, and rapid diagnosis is mandatory. Presently, the diagnostic potential of acute and latent stage TB specific antigens i.e., Rv3803c and Rv2626c was determined. Immunogenic recombinant genes of Rv3803c and Rv2626c antigens were cloned
 PMID 24295176
 Serum 25hydroxyvitamin D predicts the shortterm outcomes of Chinese patients with acute ischaemic stroke.
 Tu WJ, Zhao SJ, Xu DJ, Chen H.Author information ‡Department of Neurology, China Rehabilitation Research Center, Beijing, P.R. China.AbstractLow vitamin D levels have been reported to contribute to the risk of cardiovascular events and mortality, especially stroke. In the present study we therefore evaluated the shortterm prognostic value of serum 25(OH)D (25hydroxyvitamin D) in Chinese patients with AIS (acute ischaemic stroke). From February 2010 to September 2012, consecutive stroke patients admitted to the emergency department at two hospitals in Beijing, China were identified. Clinical information was collected, and the serum concentration of 25(OH)D and NIHSS (National Institutes of Health Stroke Scale) were measured at the time of admission. Shortterm functional outcome was measured using a modified Rankin Scale (mRS) at 90 days after admission. Multivariate analyses were performed using logistic regression models. During the inclusion period, 231 patients were diagnosed as having AIS, and 220 completed followup. The median serum 25(OH)D level was significantly lower in patients with AIS compared with normal controls [14.2 (10.218.9) ng/ml compared with 17.9 (12.522.9) ng/ml; P<0.001; values are medians (interquartile range)]. 25(OH)D was an independent prognostic marker of shortterm functional outcome and death {0.79 (0.730.85) and 0.70 (0.500.98) respectively [values are odds rations (95% confidence intervals)]; P<0.01 for both, adjusted for NHISS, other predictors and vascular risk factors} in patients with AIS. In ROC (receiver operating characteristic) curve analysis, the prognostic accuracy of 25(OH)D was higher compared with all of the other serum predictors and was in the range of NIHSS score. In conclusion, these findings suggest that 25(OH)D is an independent prognostic marker for death and functional outcome within 90 days in Chinese patients with AIS even after adjusting for possible confounding factors.
 Clinical science (London, England : 1979).Clin Sci (Lond).2014 Mar 1;126(5):33946. doi: 10.1042/CS20130284.
 Low vitamin D levels have been reported to contribute to the risk of cardiovascular events and mortality, especially stroke. In the present study we therefore evaluated the shortterm prognostic value of serum 25(OH)D (25hydroxyvitamin D) in Chinese patients with AIS (acute ischaemic stroke). From
 PMID 24020395
 Soluble urokinasetype plasminogen activator receptor as a prognostic biomarker in critically ill patients.
 Donadello K1, Scolletta S1, Taccone FS1, Covajes C1, Santonocito C1, Cortes DO1, Grazulyte D1, Gottin L2, Vincent JL3.Author information 1Department of Intensive Care, Erasme Hospital, Université Libre de Bruxelles, Brussels, Belgium.2Department of Intensive Care, Policilinico Universitario G.B. Rossi, Univerità degli Studi di Verona, Verona, Italy.3Department of Intensive Care, Erasme Hospital, Université Libre de Bruxelles, Brussels, Belgium. Electronic address: jlvincen@ulb.ac.be.AbstractPURPOSE: The aim of this study was to assess the role of blood soluble urokinasetype plasminogen activator receptor (suPAR) levels in the diagnosis and prognostication of sepsis in critically ill patients.
 Journal of critical care.J Crit Care.2014 Feb;29(1):1449. doi: 10.1016/j.jcrc.2013.08.005. Epub 2013 Oct 9.
 PURPOSE: The aim of this study was to assess the role of blood soluble urokinasetype plasminogen activator receptor (suPAR) levels in the diagnosis and prognostication of sepsis in critically ill patients.METHODS: Serum suPAR levels were measured prospectively in adult intensive care unit (ICU) pat
 PMID 24120089
和文文献
 2歳未満の鶏卵アレルギー患児への卵ボーロ負荷試験の後方視的検討
 磯崎 淳,田中 晶,菊池 信行 [他]
 臨床免疫・アレルギー科 58(2), 246250, 201208
 NAID 40019419120
 Superior segmental optic hypoplasia (SSOH)の網膜神経線維層厚の解析
 布施 昇男,相澤 奈帆子,横山 悠 [他]
 日本眼科学会雑誌 116(6), 575580, 201206
 NAID 40019336006
関連リンク
 [edit]. When using normalized units, the area under the curve (AUC) is equal to the probability that a classifier will rank a randomly chosen positive instance higher than a randomly chosen negative one ...
 ROC曲線(Receiver Operatorating Characteristic curve、受信者動作特性曲線)は、 もともとレーダーシステムの通信工学理論として開発されたものであり、レーダー信号の ノイズの中から敵機の存在を検出するための方法として開発された方法です。臨床研究 ...
関連画像
■★リンクテーブル★
リンク元  「ROC曲線」「受信者動作特性曲線」「受診者動作特性曲線」 
関連記事  「receive」「characteristic」「curve」「receiver」「cur」 
「ROC曲線」
 検査の偽陽性割合と感度のプロットから健常人と患者の識別点を評価する方法
 カットオフ値(カットオフポイント)はROC曲線の左上に近い点を採用する(感度、特異度最大の所)
「受信者動作特性曲線」
「受診者動作特性曲線」
「receive」
 v.
WordNet ［license wordnet］
「regard favorably or with disapproval; "Her new collection of poems was not well received"」WordNet ［license wordnet］
「convert into sounds or pictures; "receive the incoming radio signals"」PrepTutorEJDIC ［license prepejdic］
「〈贈与・送付されたもの〉‘を'『受け取る』,受ける / 〈情報・知識など〉‘を'知る,理解する;〈電波など〉‘を'受信する / …‘を'経験する,‘に'出くわす(meet with) / 〈被害など〉‘を'受ける,被る(suffer) / …‘を'入れる,収める(hold) / 《文》〈客など〉‘を'迎える(welcome) / 受け取る,受ける・客を迎える」WordNet ［license wordnet］
「receive a specified treatment (abstract); "These aspects of civilization do not find expression or receive an interpretation"; "His movie received a good review"; "I got nothing but trouble for my good intentions"」WordNet ［license wordnet］
「get something; come into possession of; "receive payment"; "receive a gift"; "receive letters from the front"」 同
 have
WordNet ［license wordnet］
「express willingness to have in one''s home or environs; "The community warmly received the refugees"」WordNet ［license wordnet］
「accept as true or valid; "He received Christ"」WordNet ［license wordnet］
「have or give a reception; "The lady is receiving Sunday morning"」WordNet ［license wordnet］
「partake of the Holy Eucharist sacrament」
「characteristic」
 adj.
 特徴的な、独特の、特有の、特性の、特徴ある
 n.
 関
 character、characteristically、diagnostic、discriminatory、distinct、distinction、distinctive、distinctively、feature、hallmark、particular、pathognomonic、peculiar、peculiarity、profile、property、salience、unique
WordNet ［license wordnet］
「typical or distinctive; "heard my friend''s characteristic laugh"; "red and gold are the characteristic colors of autumn"; "stripes characteristic of the zebra"」WordNet ［license wordnet］
「any measurable property of a device measured under closely specified conditions」 同
 device characteristic
WordNet ［license wordnet］
「a distinguishing quality」WordNet ［license wordnet］
「the integer part (positive or negative) of the representation of a logarithm; in the expression log 643 = 2.808 the characteristic is 2」PrepTutorEJDIC ［license prepejdic］
「『特有の』,独特の / 『特性』,特色,特微」
「curve」
 n.
 v.
WordNet ［license wordnet］
「a pitch of a baseball that is thrown with spin so that its path curves as it approaches the batter」 同
 curve ball, breaking ball, bender
WordNet ［license wordnet］
「a line on a graph representing data」PrepTutorEJDIC ［license prepejdic］
「『曲線』 / 『曲がり』,曲がったもの,湾曲部 / (野球で投球の)『カーブ』 / (…の方へ)『曲がる』,湾曲する《+『to』+『名』》 / …'を'曲げる;'を'湾曲させる」「receiver」
WordNet ［license wordnet］
「a football player who catches (or is supposed to catch) a forward pass」 同
 pass receiver, pass catcher
WordNet ［license wordnet］
「the tennis player who receives the serve」PrepTutorEJDIC ［license prepejdic］
「『受取人』 / 管財人(判決が出るまでの期間,他人の財産や事業の管理を裁判所から任命された人) / (電話の)『受話器』;(電信を)受信機;(テレビの)受像機 / (盗品の)故買人」
「cur」
WordNet ［license wordnet］
「a cowardly and despicable person」PrepTutorEJDIC ［license prepejdic］
「のら犬 / くだらない人間」