Modeling Strategy

Document methodologies, experiments, and key decisions related to modelling. Replace placeholders with actual project information as it becomes available.

Problem Framing

• Prediction target: describe the response variable or classification objective.
• Business KPIs: connect model performance to business impact.
• Constraints: latency, interpretability requirements, fairness considerations.

Baseline Model

• Summarise the baseline approach (e.g., linear regression, simple classifier).
• Provide a short code snippet or reference to the implementation (train_linear_model).
• Capture baseline metrics and why they serve as a starting point.

Feature Engineering

• List primary features and their derivations.
• Note encodings, scaling, interactions, or domain-specific transformations.
• Mention tools or libraries used (pandas, sklearn, featuretools, etc.).

Experiment Tracking

Experiment ID	Description	Model/Params	CV/Validation Scheme	Metrics	Notes
EXP-001	Baseline linear regression	Normal equation	Train/test split	RMSE = …	Baseline
EXP-002	Random Forest	n_estimators=200	5-fold CV	RMSE = …	Improvement

Update this table as new experiments are run. Link to notebooks, config files, or pipeline runs.

Evaluation Strategy

• Validation approach (train/validation split, cross-validation, rolling windows).
• Performance metrics (RMSE, MAE, accuracy, precision/recall, F1, ROC-AUC).
• Thresholds for success and decisions for model selection.

Advanced Techniques (Optional)

• Hyperparameter tuning methodologies (GridSearch, Bayesian optimisation, Optuna).
• Ensembling or stacking strategies.
• Model explainability (SHAP, LIME, feature importance).
• Fairness checks or bias mitigation steps.

Deployment Considerations

• How the model will be served (batch scoring, API endpoints, embedded in Product tool).
• Monitoring requirements (data drift, performance regression).
• Rollback plans or champion/challenger setups.