.. _nnodely-validation: Validation ========== Model validation in **nnodely** follows established practices in mechanical and control engineering, combining predictive accuracy assessment with explicit consideration of model complexity. Validation is typically performed on a dedicated test set and can be integrated into the training workflow for continuous performance monitoring using the :doc:`Validator module ` and the :func:`analyzeModel ` function. .. rubric:: Training Feedback and Reporting During training and validation, **nnodely** provides both textual and graphical feedback through configurable visualization backends. These tools enable users to inspect: - Convergence behavior - Loss evolution - Quality of model fitting This feedback facilitates early detection of overfitting, instability, or poor generalization. Validation results can also be exported as a structured PDF report. The report includes loss trends, learned fuzzy and parametric functions, and additional diagnostic metrics generated during :func:`analyzeModel `, supporting systematic post-analysis and long-term documentation. .. rubric:: Domain-Specific Validation Metrics In many applications, domain-specific performance indicators are essential for reliable model assessment. In physical and control systems, validation may involve: - Frequency-domain analysis - Stability margins - Control-oriented performance indices These analyses can be performed within the validation workflow using :func:`analyzeModel ` with appropriate configuration of prediction horizons, closed-loop connections, and batch parameters. These metrics are not commonly used in conventional machine learning but are critical for evaluating models in engineering contexts. By incorporating both general-purpose and domain-specific validation criteria, **nnodely** ensures that trained models are evaluated according to the requirements of the target application. .. toctree:: :maxdepth: 1 validator_module