Why this decision matters
Algorithm quality depends on whether data represents real conditions, metrics reflect business priorities and results can be reproduced in the target environment.
Confirm data sources, sample coverage, label quality, metric definitions, error costs and deployment constraints before using a score as acceptance evidence.
Conditions to confirm before development
- Representative normal, difficult and rare samples
- Separated train, validation and independent test sets
- Metrics tied to false-positive, false-negative or error costs
- Target runtime, latency, resource and stability requirements
Implementation and delivery approach
Create a baseline, evaluate candidate methods on fixed data and analyze error categories before optimizing or integrating the algorithm.
Retain data versions, labeling rules, metric code, model version, configuration and failed examples for reproduction.
Acceptance boundary
This guidance applies to the agreed data, system and environment. Project-specific scope, dependencies and acceptance conditions must be confirmed separately.