Phase 05 of the Data Science Lifecycle

Model Quality -
systematically and rigorously ensured

Before a model goes into production, it must prove its performance - against your business goals.

Back to Overview Phase 04 Phase 06

Model validation

Trust Through Transparent Evaluation

We evaluate against the criteria that matter to your business - and make decisions traceable.

Business Relevance

Evaluation against business-relevant criteria, not just technical metrics.

Explainability

We show why a model arrives at certain results.

Robustness & Fairness

Checking for biases and behaviour under realistic conditions.

Model validationvalidated · explainable · fair

Confusion Matrix

PREDICTED

ACTUAL

847

438

Key Metrics

Accuracy98.85%

Precision97.33%

Recall99.32%

F1 Score98.31%

AUC-ROC99.71%

ROC Curve

Feature Importance (SHAP)

feature_primary

82%

feature_temporal

64%

feature_interaction

51%

feature_categorical

38%

feature_derived

25%

GO ✓

All thresholds passed

Ready for deployment

Data science project approach

Our Approach

Technical Evaluation

Comprehensive analysis using suitable metrics aligned with your success criteria.

Interpretability

Examining influencing factors and whether the model has learned the right patterns.

Robustness Testing

Tests under realistic and challenging conditions.

Business Validation

Verifying whether the model can deliver the expected value.

Data science deliverables

Typical Deliverables

Evaluation report with business metrics

Model transparency analysis

Robustness and fairness assessment

Recommendation for next steps