The final step is to choose your metrics based on your problem and data. There is no one-size-fits-all metric that works for every problem. You may need to use multiple metrics to capture different aspects of your solution, such as accuracy, robustness, interpretability, or fairness. You may also need to trade-off between different metrics, such as precision and recall, or speed and quality. The best metrics are those that align with your problem objectives, reflect your data characteristics, and provide actionable insights for improvement. For example, if your problem is to detect fraud, you may use metrics such as recall, precision-recall curve, or cost-sensitive loss. If your problem is to recommend products, you may use metrics such as precision at k, recall at k, or NDCG.
Choosing the best metrics to rank problems is not a trivial task. It requires a clear understanding of your problem, data, and goals. By following these steps, you can select the most suitable metrics for your machine learning project and optimize your solutions accordingly.