# The use of the area under the ROC curve in the evaluation of machine learning algorithms

## Summary (1 min read)

### Summary

- In this paper the authors investigate the use of receiver operating characteristic ROC curve for the evaluation of machine learning algorithms.
- In particular the authors investigate the use of the area under the ROC curve AUC as a measure of classi er performance.
- The machine learning algorithms used are chosen to be representative of those in common use two decision trees.

### C and Multiscale Classi er two neural networks Perceptron and Multi layer Perceptron

- And two statistical methods K Nearest Neighbours and a Quadratic Discriminant Function.
- The evaluation is done using six real world medical diagnostics data sets that contain a varying numbers of inputs and samples but are primarily continuous input binary classi cation problems.
- The authors identify three forms of bias that can a ect comparisons of this type estimation selection and expert bias and detail the methods used to avoid them.
- The authors use this equivalence to show that the standard deviation of AUC estimated using fold cross validation is a reliable estimator of the standard error estimated using the Wilcoxon test.

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##### Citations

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### Cites methods from "The use of the area under the ROC c..."

...A common method is to calculate the area under the ROC curve, abbreviated AUC (Bradley, 1997; Hanley and McNeil, 1982)....

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11,512 citations

5,063 citations

### Cites methods from "The use of the area under the ROC c..."

...Often, the area under the curve is used as a simple metric to define how an algorithm performs over the whole space (Bradley, 1997; Davis et al., 2005; Goadrich et al., 2004; Kok & Domingos, 2005; Macskassy & Provost, 2005; Singla & Domingos, 2005)....

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2,800 citations

### Cites background from "The use of the area under the ROC c..."

...ROC curve [9] is one of the popular metrics to evaluate the learners for imbalanced data sets....

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...AUC (Area under ROC) can also be applied to evaluate the imbalanced data sets [9]....

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##### References

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