ADVANCED // MULTI DOMAIN
MODULE 12 // ML ENGINEERING
Model Customization.
Customize behavioral models for domain-specific patterns.
MODEL STRATEGY
Domain-specific models improve accuracy by incorporating domain knowledge. But building from scratch wastes universal pattern intelligence. Transfer learning bridges this gap.
Customization approaches: feature engineering (add domain-specific features), threshold tuning (adjust sensitivity for domain risk profile), and model fine-tuning (adapt weights for domain data).
Continuous improvement: domain models should improve with usage. Implement feedback loops that incorporate confirmed fraud and false positive data.
BASE
Universal Model
Pre-trained model with cross-domain patterns. Immediate deployment, general accuracy.
TUNED
Threshold Tuning
Universal model with domain-specific thresholds. Quick customization, moderate improvement.
CUSTOM
Fine-Tuned Model
Model adapted with domain data. Best accuracy, requires training data and expertise.
KNOWLEDGE CHECK // Q12
Why start with universal models rather than building domain-specific models from scratch?