Paper Citation Record · LEDGER

Interactive Classification Metrics: A graphical application to build robust intuition for classification model evaluation

As of 17 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:2412.17066.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.17066 v1

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T05:52:12.113053Z

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

17 of 17 outbound references displayed

verified exact1
verified fuzzy9
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 025338a0-6ab8-4d39-a47e-354c1b84b714 · outbound

This paper cites Occupational outlook handbook, data scientists,.

Interactive Classification Metrics: A graphical application to build robust intuition for classification model evaluation Occupational outlook handbook, data scientists,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:52:12.460002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a8bd8ed6-734f-49a0-9832-8bb6610d56a6 · outbound

This paper cites State of data science and machine learning,.

Interactive Classification Metrics: A graphical application to build robust intuition for classification model evaluation State of data science and machine learning,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:52:12.447612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a79625eb-076e-443c-baea-f48e589b960a · outbound

This paper cites A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis,.

Interactive Classification Metrics: A graphical application to build robust intuition for classification model evaluation A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis,

Reference 3

Resolution: verified exact; doi, observed 2026-08-11T05:52:12.172939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 251f1df3-1cf7-429e-a20d-6bb977ae3458 · outbound

This paper cites Leakage and the reproducibility crisis in machine- learning-based science,.

Interactive Classification Metrics: A graphical application to build robust intuition for classification model evaluation Leakage and the reproducibility crisis in machine- learning-based science,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T05:52:12.057649Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ce2c8ac-9eca-4a92-bd92-6b9909d660f3 · outbound

This paper cites Illusory generalizability of clinical prediction models,.

Interactive Classification Metrics: A graphical application to build robust intuition for classification model evaluation Illusory generalizability of clinical prediction models,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T05:52:12.061842Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb4f0ae6-360e-4dd8-b2c0-ad178eef6ac9 · outbound

This paper cites The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification,.

Interactive Classification Metrics: A graphical application to build robust intuition for classification model evaluation The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T05:52:12.066376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3472bb26-aa41-421c-9340-2b0897dd14f5 · outbound

This paper cites Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation.

Interactive Classification Metrics: A graphical application to build robust intuition for classification model evaluation Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T05:52:12.071594Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a638ce53-e9d3-46b1-a59c-63b9cd05d69d · outbound

This paper cites The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation,.

Interactive Classification Metrics: A graphical application to build robust intuition for classification model evaluation The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T05:52:12.075956Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8b6daf8-5456-40e8-934c-6fe7c6b9eca3 · outbound

This paper cites The MCC-F1 curve: a performance evaluation technique for binary classification.

Interactive Classification Metrics: A graphical application to build robust intuition for classification model evaluation The MCC-F1 curve: a performance evaluation technique for binary classification

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T05:52:12.079742Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d331c191-8b60-4415-955d-ec8ebe7d4a19 · outbound

This paper cites Table of confusion.

Interactive Classification Metrics: A graphical application to build robust intuition for classification model evaluation Table of confusion

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:52:12.436110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 73807d4d-e190-4c8f-a115-cfaf78e9a4b1 · outbound

This paper cites ROC and AUC: A visual explanation of receiver operating characteristic curves and area under the curve,.

Interactive Classification Metrics: A graphical application to build robust intuition for classification model evaluation ROC and AUC: A visual explanation of receiver operating characteristic curves and area under the curve,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:52:12.423400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 810ac4d9-893e-4933-9a86-013738cda5f4 · outbound

This paper cites Precision and recall: Accuracy is not enough,.

Interactive Classification Metrics: A graphical application to build robust intuition for classification model evaluation Precision and recall: Accuracy is not enough,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:52:12.410710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 666287ca-560b-4890-9175-f6cd26462aff · outbound

This paper cites ROC curves,.

Interactive Classification Metrics: A graphical application to build robust intuition for classification model evaluation ROC curves,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:52:12.397670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 169213b1-4555-4a6f-b8f9-6469c116ba0c · outbound

This paper cites Understanding ROC curves,.

Interactive Classification Metrics: A graphical application to build robust intuition for classification model evaluation Understanding ROC curves,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:52:12.385341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9f2ea1a1-8617-4c08-bd3a-67e5db19a28b · outbound

This paper cites Classification: Accuracy, recall, precision, and related metrics,.

Interactive Classification Metrics: A graphical application to build robust intuition for classification model evaluation Classification: Accuracy, recall, precision, and related metrics,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:52:12.373686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c5c4f2f5-417c-4ea8-9ab6-77a63a5dd957 · outbound

This paper cites Principles of effective data visualization,.

Interactive Classification Metrics: A graphical application to build robust intuition for classification model evaluation Principles of effective data visualization,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T05:52:12.113053Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c58adc93-eca3-41c2-967a-b14279aaa6c0 · outbound

This paper cites Available: https://developers.google.com/machine-learning/crash-course/ classification/accuracy-precision-recall.

Interactive Classification Metrics: A graphical application to build robust intuition for classification model evaluation Available: https://developers.google.com/machine-learning/crash-course/ classification/accuracy-precision-recall

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:52:12.360720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

No inbound Pith citation observations are available.