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CITATION.cff
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cff-version: 1.2.0
title: AUC Calculator
message: >-
Please cite this software using the metadata from
'preferred-citation'.
type: software
authors:
- given-names: Jesse
family-names: Davis
affiliation: KU Leuven
email: [email protected]
- given-names: Mark
family-names: Goadrich
email: [email protected]
affiliation: Hendrix College
- given-names: Alexander
family-names: Hayes
email: [email protected]
affiliation: Indiana University Bloomington
identifiers:
- type: doi
value: 10.1145/1143844.1143874
description: >-
The relationship between Precision-Recall and
ROC curves
repository-code: 'https://github.com/srlearn/AUCCalculator'
abstract: >-
Receiver Operator Characteristic (ROC) curves are
commonly used to present results for binary
decision problems in machine learning. However,
when dealing with highly skewed datasets,
Precision-Recall (PR) curves give a more
informative picture of an algorithm's performance.
We show that a deep connection exists between ROC
space and PR space, such that a curve dominates in
ROC space if and only if it dominates in PR space.
A corollary is the notion of an achievable PR
curve, which has properties much like the convex
hull in ROC space; we show an efficient algorithm
for computing this curve. Finally, we also note
differences in the two types of curves are
significant for algorithm design. For example, in
PR space it is incorrect to linearly interpolate
between points. Furthermore, algorithms that
optimize the area under the ROC curve are not
guaranteed to optimize the area under the PR curve.
keywords:
- metrics
- machine-learning
license: MIT
preferred-citation:
type: conference-paper
authors:
- family-names: "Davis"
given-names: "Jesse"
- family-names: "Goadrich"
given-names: "Mark"
title: "The relationship between Precision-Recall and ROC curves"
collection-title: "Proceedings of the 23rd International Conference on Machine Learning"
conference:
name: "ICML 2006"
publisher:
name: "Association for Computing Machinery"
address: "New York, NY, USA"
location:
name: "23rd International Conference on Machine Learning"
city: "Pittsburgh, Pennsylvania"
country: "US"
doi: "10.1145/1143844.1143874"
isbn: "1595933832"
start: 233
end: 240
year: 2006