Glossary

Model scoring and deployment

Definition

Scoring is applying a trained model to new data — assigning a prediction or probability to every customer, sample or product. Deployment means putting the model into routine operation: scoring data from a database regularly, monitoring its accuracy over time and retraining it when reality changes. Only a deployed model delivers value.

Data mining & machine learning

The model is exported in a standard format (PMML) or as code (C, C++, SQL, Python), or runs directly on a server scoring in batch or in real time. Deployment includes monitoring: a shift in input distributions (drift) and a drop in accuracy signal the need to retrain.

Practical rules: version models and data, document predictors and their preparation, and involve operations experts — a model nobody understands goes unused. In-database scoring saves data transfer with millions of records.

In Statistica

In the Data Mining menu the Rapid Deployment of Predictive Models node scores new data with models stored as PMML, including comparison of several models; Statistica exports models as PMML, C/C++ or SQL, and editions with server deployment (Statistica Enterprise) schedule automatic scoring of database data with monitoring.

Related terms

Knowledgebase guides

FAQ

What is PMML?
Predictive Model Markup Language — an open XML format for exchanging predictive models between tools, which Statistica both reads and writes.
How often should a model be retrained?
When monitoring shows input drift or falling accuracy; in dynamic domains (marketing, risk) typically quarterly, in stable processes yearly.

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