Binary Label Analysis Report

A Binary Label Analysis Report is a labeled data analysis report that documents findings from a binary label analysis task on binary labeled datasets.

• AKA: Two-Class Label Report, Binary Classification Analysis Report, Positive-Negative Analysis Summary, Binary Annotation Report.
• Context:
  • It can typically present Binary Distribution Statistics including positive-negative counts, class ratios, and percentage breakdowns.
  • It can typically display Binary Balance Visualizations via proportion pie charts, ratio bar graphs, and distribution histograms.
  • It can typically document Binary Boundary Analysis through edge case examples, ambiguous instances, and confidence distributions.
  • It can typically summarize Binary Agreement Metrics including inter-rater reliability, annotation consensus, and disputed case percentages.
  • It can typically provide Binary Classification Readiness assessment via balance evaluation, coverage analysis, and quality scoring.
  • ...
  • It can often include Threshold Analysis Sections showing optimal cutoff points, sensitivity-specificity tradeoffs, and ROC curves.
  • It can often contain Bias Detection Findings documenting systematic preferences, annotation tendencies, and collection skews.
  • It can often feature Difficulty Analysis highlighting hard positive examples, hard negative examples, and borderline cases.
  • It can often incorporate Recommendation Sections suggesting balancing strategies, threshold settings, and collection priorities.
  • ...
  • It can range from being a Basic Binary Report to being a Detailed Binary Report, depending on its analysis comprehensiveness.
  • It can range from being a Balanced Binary Report to being an Imbalanced Binary Report, depending on its class distribution focus.
  • It can range from being a Technical Binary Report to being a Business Binary Report, depending on its audience orientation.
  • It can range from being a Static Binary Report to being an Interactive Binary Report, depending on its presentation format.
  • It can range from being a Standalone Binary Report to being a Comparative Binary Report, depending on its analysis context.
  • ...
  • It can be generated by a Binary Label Analysis System after completing a binary label analysis task.
  • It can inform Binary Classifier Development through training recommendations and evaluation guidance.
  • It can support Annotation Improvement via quality issue identification and process refinement suggestions.
  • It can guide Data Collection Planning through balance target specification and coverage gap identification.
  • It can integrate with Model Development Pipelines for readiness assessment and risk evaluation.
  • ...
• Example(s):
  • Sentiment Binary Analysis Reports, such as:
    • Product Review Binary Reports, such as:
      • Amazon Review Sentiment Report showing 70-30 positive-negative split.
      • App Store Rating Binary Report documenting satisfaction distribution.
      • Restaurant Review Polarity Report presenting favorable-unfavorable ratio.
    • Social Media Binary Reports, such as:
      • Twitter Sentiment Binary Report documenting opinion polarity distribution.
      • Brand Mention Sentiment Report showing positive-negative mention ratio.
      • Campaign Response Binary Report presenting support-opposition analysis.
  • Detection Binary Analysis Reports, such as:
    • Spam Detection Binary Reports, such as:
      • Email Spam Analysis Report documenting 99.5% legitimate email finding.
      • SMS Spam Binary Report showing spam message characteristics.
      • Forum Spam Detection Report presenting genuine-spam post ratio.
    • Fraud Detection Binary Reports, such as:
      • Credit Card Fraud Report documenting 0.1% fraud transaction rate.
      • Insurance Fraud Binary Report showing claim legitimacy distribution.
      • Account Takeover Report presenting genuine-compromised account ratio.
  • Medical Binary Analysis Reports, such as:
    • Diagnostic Binary Reports, such as:
      • COVID Test Result Report documenting positive-negative test ratio.
      • Cancer Screening Binary Report showing malignant-benign distribution.
      • Diabetes Risk Binary Report presenting high-low risk classification.
    • Treatment Binary Reports, such as:
      • Drug Response Binary Report documenting responder-nonresponder ratio.
      • Therapy Effectiveness Report showing success-failure outcome distribution.
      • Side Effect Binary Report presenting occurrence-absence frequency.
  • Quality Binary Analysis Reports, such as:
    • Manufacturing Binary Reports, such as:
      • Quality Control Binary Report documenting pass-fail inspection ratio.
      • Defect Detection Report showing defective-nondefective distribution.
      • Compliance Check Binary Report presenting conforming-nonconforming rate.
    • Service Binary Reports, such as:
      • Customer Satisfaction Binary Report documenting satisfied-dissatisfied ratio.
      • SLA Compliance Report showing met-missed target distribution.
      • Issue Resolution Binary Report presenting resolved-unresolved case ratio.
  • Security Binary Analysis Reports, such as:
    • Threat Binary Reports, such as:
      • Network Intrusion Report documenting attack-normal traffic ratio.
      • Malware Detection Binary Report showing infected-clean file distribution.
      • Phishing Email Report presenting phishing-legitimate email ratio.
    • Access Binary Reports, such as:
      • Login Attempt Binary Report documenting successful-failed authentication ratio.
      • Permission Check Report showing authorized-unauthorized access distribution.
      • Behavior Anomaly Report presenting normal-suspicious activity ratio.
  • ...
• Counter-Example(s):
  • Multi-Class Analysis Report, which documents multiple class findings rather than binary distributions.
  • Regression Analysis Report, which presents continuous value analysis rather than binary category results.
  • Feature Analysis Report, which shows data characteristics rather than label distributions.
  • Model Performance Report, which documents prediction accuracy rather than label analysis.
  • Annotation Guideline, which provides labeling instructions rather than analysis findings.
• See: Binary Label Analysis Task, Labeled Data Analysis Report, Label Balance Report, Label Distribution Report, Binary Classification Report, ROC Analysis Report.

---