Define what the prediction changes
Classification, risk scoring, propensity, anomaly detection and other models only create value when someone or some system acts differently because of the output. We define that action first.
SC-Analytics · Services
Machine learning consulting focused on measurable decision improvement
SC-Analytics builds predictive models when prediction can improve a defined business decision. We make the baseline, error costs, validation design and operating context explicit before choosing model complexity.
Validate against the real future
Data leakage and optimistic validation can make a model look better than it will operate. We use time-aware or process-aware validation and compare performance to a meaningful baseline.
Deploy with thresholds and monitoring
Production use requires thresholds, calibration, drift checks, explanations where relevant, ownership and a way to handle uncertain cases. Those design choices are part of the model, not an afterthought.
Have a prediction problem tied to a real decision?
We can assess whether machine learning is justified, what baseline it must beat and how the output should be used in the operating process.