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Test case

concept · part of Model Testing

A test case is a safety net for machine learning models, ensuring they perform not only in ideal conditions but also in real-world, unpredictable scenarios. It helps identify bugs, validate generalization, catch edge cases, and ensure pipeline robustness. Key components: input data (typical, unusual, extreme), expected output (clearly defined prediction or behavior), and test conditions (performance under load, edge cases, incomplete/noisy data).

Edge cases are unusual or extreme inputs that challenge model limitations. Examples from a credit risk model: missing data or incorrect formats (e.g., incomplete financial records, non-standard employment data), extreme values (e.g., high net worth individuals with income/assets far outside typical range), unseen categories (e.g., new regions or gig economy workers not in training data), and large data sets (e.g., sudden surge in applications pushing system memory limits). Designing test cases for these scenarios ensures model resilience to real-world inputs.

Test cases can be organized into a test suite for systematic validation. They should be automated where possible to enable frequent regression testing. Each test case should be independent and self-contained, with a clear pass/fail criterion.

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