Why Random Splits Mislead: Validating Models the Way They Will Be Used
A model can score brilliantly in testing and disappoint in production because the test did not resemble real use. How to design validation that tells the truth.
Insight topic
Machine Learning insights
Modelling choices, data requirements and validation for machine learning work. Articles in this topic are written for leaders and practitioners who need practical, evidence-based guidance.
2 articles in Machine Learning
Measuring Document AI Accuracy at Field Level
An extraction system can be 95 percent accurate and still get the invoice amount wrong. Why field-level measurement drives better automation decisions.
Topic guide
Understanding machine learning
Machine learning turns data into predictions, classifications and rankings. This topic covers the modelling and validation choices that separate models that work in testing from models that work in use.
FAQ
Key questions about machine learning
Why do machine learning models underperform after deployment?
Common causes are validation that does not reflect real use, features containing information unavailable at prediction time, and changes in data after training.
Are complex models always better?
No. For structured business data, well-engineered simpler models often perform as well and are easier to explain and maintain.
How often should models be retrained?
When monitoring shows accuracy declining or input data shifting, or on a schedule suited to how quickly the underlying patterns change.
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