ML0035 Model Comparison

Compare machine learning models by defining clear objectives and metrics, using consistent data splits, training and tuning each model, and evaluating them through robust metrics and statistical tests. Finally, consider trade-offs like model complexity and interpretability to make an informed choice.
(1) Choose Relevant Metrics: Select evaluation metrics that align with your task (e.g., accuracy or F1 for classification)
Below shows an example using ROC curves for model comparison.

(2) Use Consistent Data Splits: Evaluate all models on the same train/validation/test splits—or identical cross-validation folds—to ensure fairness
(3) Apply Cross-Validation: Employ k-fold or nested cross-validation to reduce variance in performance estimates, especially with limited data
(4) Control Randomness: Run each model multiple times with different random seeds (data shuffles, weight initializations) and average the results to gauge stability
(5) Perform Statistical Tests: Use paired tests to determine if observed differences are statistically significant
(6) Measure Efficiency: Record training time, inference latency, and resource usage (CPU/GPU and memory) to assess practical deployability
(7) Evaluate Robustness & Interpretability: Test models under data perturbations or adversarial noise, and compare explainability