ROC curve
ROC Curve
The ROC curve (pronounced /ɑːr oʊ siː kɜːrv/), or Receiver Operating Characteristic 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.
Etymology
The term "ROC" originated from signal detection theory developed during World War II for the analysis of radar signals. The name "Receiver Operating Characteristic" was coined as it reflected the ability of the radar receiver operator to correctly identify enemy aircraft.
Related Terms
- True Positive Rate: Also known as sensitivity, it measures the proportion of actual positives that are correctly identified as such.
- False Positive Rate: Also known as fall-out, it measures the proportion of actual negatives that are incorrectly identified as positives.
- Binary Classifier: A type of classification model that categorizes data into one of two groups.
- Discrimination Threshold: The point of separation where the probabilities of belonging to either of the two groups are equal.
Usage
The ROC curve is widely used in medicine, radiology, biometrics and various fields of machine learning. It is a popular tool for predictive models that need to balance sensitivity and specificity.
Calculation
The ROC curve is generated by plotting the cumulative distribution function (area under the probability distribution from minus infinity to the discrimination threshold) of the true positive rate, against the cumulative distribution function of the false positive rate, at various threshold settings.
Advantages
The ROC curve is a comprehensive and flexible tool. It allows the user to select the optimal model based on their specific cost/benefit tradeoff. It also allows for the comparison of different models.
Disadvantages
The ROC curve does not provide a single metric of performance, which can make model selection difficult. It also assumes that the costs of false positives and false negatives are roughly equal, which may not be the case in all applications.
External links
- Medical encyclopedia article on ROC curve
- Wikipedia's article - ROC curve
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