Processes in this Phase
Modeling Technique Selection
Select appropriate algorithms and modeling approaches based on problem type, data characteristics, and business requirements.
Test Design
Design comprehensive testing strategy including cross-validation, holdout sets, and evaluation metrics aligned with success criteria.
Model Building
Develop and train machine learning models using prepared data, implementing selected algorithms and architectures.
Model Assessment
Evaluate model performance against test data using defined metrics. Compare results across different modeling approaches.
Hyperparameter Tuning
Optimize model parameters using systematic search strategies to improve performance and generalization.
Ensemble Methods
Combine multiple models to improve predictive performance through bagging, boosting, or stacking techniques.
Model Interpretability
Apply explainability techniques to understand model decisions and ensure transparency for stakeholders.
Model Documentation
Create comprehensive documentation including model cards, training procedures, and performance characteristics.