This lesson develops controlled execution of Predictive Scenarios for Classification, Regression, and Time Series. The work is grounded in a commercial analytics team seeking defensible forecasts, driver analysis, and actionable insights from changing sales and margin data and contributes directly to explainable augmented analytics.
Using the scenario, the instructor demonstrates classification scenarios, regression models, time-series forecasting, model metrics and highlights object relationships, determination behavior, integration effects, and approval evidence. The demonstration deliberately introduces the exception where the selected model scores well globally but performs poorly for a priority customer segment.
Complete a controlled activity covering classification scenarios, regression models, time-series forecasting, model metrics and preserve the result in predictive scenario comparison pack.
Start at the earliest point where expected behavior differs from the observed result. Review process sequence, relevant data, configuration, authorization, integration, and timing as separate hypotheses. Treat “the selected model scores well globally but performs poorly for a priority customer segment” as unconfirmed until settings, documents, logs, reports, and focused retesting support the cause.
Learners can apply Predictive Scenarios for Classification, Regression, and Time Series, explain the resulting SAP behavior, and defend the outcome through predictive scenario comparison pack.