AI Computing Power · KAEL Editorial Department
Why data quality is often more important than model complexity
Complex models can express more relationships and may more easily absorb errors in the data. Establishing clear data rules is the basis for understanding experimental results.
Check input first
Missing records, duplicate samples, and time misalignments can alter the results. Before cleaning, save the original version and record how much data has been deleted or modified by each rule to avoid data processing becoming an unexplainable black box.
Keep the caliber consistent
Fields with the same name may have different meanings in different sources. Unify the unit, time base and update frequency, and then decide how to combine them; you cannot directly splice them just because the column names are the same.
Maintain traceability
Each experiment is associated with data versions, processing code, and configurations. When the results change, first confirm whether the input has changed, and then determine whether the model has improved. Traceable data chains can reduce a lot of invalid debugging.
