Why choose this project topic?
This topic turns choosing evaluation measures that remain informative for infrequent outcomes into a measurable analytical question. Comparing metric sensitivity, ranking reversals and uncertainty gives a student material for visual results, methodological criticism and a discussion of how the data's limitations change the conclusion.
How does rare event metric comparison affect choosing evaluation measures that remain informative for infrequent outcomes, as measured by metric sensitivity, ranking reversals and uncertainty?
Choose an accessible licensed or synthetic dataset, agree the unit of analysis and narrow choosing evaluation measures that remain informative for infrequent outcomes to a reproducible comparison your supervisor can review.
Proposed project objectives
- 01Define the data, assumptions and comparison design for choosing evaluation measures that remain informative for infrequent outcomes.
- 02Generate synthetic rare-event predictions, compare accuracy, precision-recall and cost-based metrics under changing prevalence.
- 03Compare metric sensitivity, ranking reversals and uncertainty and report uncertainty and sensitivity to analytical choices.
A suggested research approach
Generate synthetic rare-event predictions, compare accuracy, precision-recall and cost-based metrics under changing prevalence. Confirm the data licence and variable definitions before analysis. Preserve an untouched evaluation set where relevant, document exclusions and missingness, and report metric sensitivity, ranking reversals and uncertainty with uncertainty or sensitivity checks rather than selecting only favourable results.
What you will need
- Synthetic prediction scores
- Prevalence scenarios
- Metric calculation tools
Keep your project scope clear
Cost assumptions are illustrative and must not be applied to high-stakes decisions without domain review.
Data Science project chapter outline
Use this outline as a starting point. You can edit the chapter titles to match your department’s format during setup.
- Chapter 1Introduction
- Chapter 2Literature Review
- Chapter 3Research Methodology
- Chapter 4Presentation and Analysis of Results
- Chapter 5Summary, Conclusion and Recommendations
Turn this topic into your own final year project.
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