Neural network performance is evaluated using metrics tailored to the task. For classification, metrics like accuracy, precision, recall, and F1-score provide insights into model effectiveness. For regression, metrics like Mean Squared Error (MSE) or Mean Absolute Error (MAE) are commonly used.
Validation and test sets assess the model's ability to generalize to unseen data. Cross-validation further ensures robustness by testing the model on multiple data splits. This is particularly useful for small datasets.
Visualization techniques like confusion matrices (for classification) or residual plots (for regression) provide additional insights. For complex models, interpretability tools like SHAP or LIME can help identify strengths and weaknesses, guiding further optimization.