How do you choose the right evaluation metrics for your ML problem?

1 Define your problem type

The first step is to define what kind of problem you are trying to solve with ML. Is it a classification problem, where you want to assign labels to instances? Is it a regression problem, where you want to predict a continuous value? Is it a clustering problem, where you want to group similar instances? Each problem type has different evaluation metrics that are suitable for measuring the accuracy, error, or similarity of your predictions.

2 Identify your stakeholders and goals

The next step is to identify who are the stakeholders of your solution and what are their goals and expectations. Who will use your solution and how? What are the benefits and risks of your solution? What are the success criteria and the performance thresholds? By understanding your stakeholders and goals, you can align your evaluation metrics with the value and impact of your solution.

3 Choose relevant and interpretable metrics

The third step is to choose evaluation metrics that are relevant and interpretable for your problem type, stakeholders, and goals. You want to select metrics that capture the essence of your solution and reflect its quality and usefulness. You also want to select metrics that are easy to understand and communicate to your stakeholders. For example, if you are solving a classification problem, you might want to use metrics such as accuracy, precision, recall, or F1-score, depending on the nature and balance of your classes.

4 Consider multiple and complementary metrics

The fourth step is to consider using multiple and complementary evaluation metrics for your ML problem. You might want to use different metrics for different aspects or stages of your solution, such as training, validation, testing, or deployment. You might also want to use different metrics for different perspectives or scenarios of your solution, such as best-case, worst-case, or average-case. By using multiple and complementary metrics, you can gain a more comprehensive and robust understanding of your solution's performance and limitations.

5 Evaluate the trade-offs between metrics

The final step is to evaluate the trade-offs between different evaluation metrics for your ML problem. You might find that some metrics are conflicting or competing with each other, such as accuracy and interpretability, or precision and recall. You might also find that some metrics are more sensitive or robust to changes in your data, model, or parameters. You need to weigh the pros and cons of each metric and decide which ones are more important or relevant for your solution.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?