What are some common metrics used to evaluate the performance of a machine learning model?

In my final year at university, I worked on a project where we developed a model to classify emails as spam or not. To evaluate our model, we used several metrics including accuracy, precision, and recall. Accuracy gave us a basic idea of overall performance, but when we looked deeper, we found that precision was critical because we wanted to minimize false positives—email users don’t want legitimate emails marked as spam. By focusing on recall as well, we ensured that we captured as many spam emails as possible. This project taught me how using multiple metrics can provide a more comprehensive view of model performance.

During my time volunteering for a local nonprofit, I assisted in analyzing survey data to understand community needs. I learned to use metrics like Net Promoter Score (NPS) and response rates to evaluate the effectiveness of our outreach efforts. While NPS helped gauge overall satisfaction, the response rate provided insight into how well we engaged with our audience. This experience highlighted the importance of not just gathering data but also analyzing it with the right metrics to inform future strategies and improve community support.

In my internship at a tech startup, I was involved in a project that aimed to predict housing prices. We used metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) to evaluate our regression model. I learned that while RMSE is sensitive to outliers, MAE provided a straightforward interpretation of average errors. This dual approach helped us to refine our model effectively, ensuring we communicated results clearly to stakeholders. It was a great introduction to the real-world implications of choosing the right performance metrics.