Machine Learning Foundations

objective

Execute machine learning foundations work with reproducible research, explicit controls, and deployable outputs.

workflow

1. •define assumptions, governing equations, and boundary conditions.
2. •estimate parameters with reproducible calibration settings.
3. •validate residual structure, numerical stability, and convergence behavior.
4. •stress model behavior across regime changes and parameter perturbations.
5. •release only when out-of-sample accuracy and stability remain within limits.

required diagnostics

• •residual diagnostics and autocorrelation by horizon.
• •parameter stability across rolling and expanding windows.
• •numerical convergence behavior and solver tolerance sensitivity.
• •forecast calibration and distributional fit checks.
• •generalization gap under rolling retraining
• •feature drift and leakage diagnostics

risk controls

• •enforce parameter-bound and convergence-failure safeguards.
• •enforce rollback to baseline models on instability.
• •enforce monitoring for drift and structural-break detection.

outputs

• •run python scripts/machine_learning_foundations_diagnostics.py input.csv --output diagnostics.json and keep the json artifact.
• •write an implementation memo using references/machine-learning-foundations-playbook.md with assumptions, tests, limits, and rollout plan.

resources

• •use scripts/machine_learning_foundations_diagnostics.py for deterministic diagnostics.
• •use references/machine-learning-foundations-playbook.md for the domain-specific checklist and delivery structure.