What are some common techniques for model evaluation, and how do you determine which metric to use for a given problem?

In my final year of college, I worked on a project where we developed a classification model to predict student performance based on their study habits and attendance. We initially used accuracy as our evaluation metric, but quickly realized it wasn’t sufficient due to the imbalanced nature of our dataset, where many students performed well while a few struggled. To address this, we switched to using F1-score, which balanced precision and recall, giving us a better insight into our model's performance. By the end of the project, we not only improved our model's effectiveness but also learned how critical it is to choose the right metric based on the problem context.

While volunteering for a non-profit organization, I was part of a team analyzing the effectiveness of our fundraising campaigns. We initially tracked the total funds raised as our success metric, but we soon discovered it didn't capture the full picture. We then introduced additional metrics like conversion rate and average donation size, which provided deeper insights into donor engagement. This experience taught me the importance of tailoring evaluation metrics to the goals of a project, ensuring that we not only measure success but also understand the factors contributing to it.

In my first role as a junior data analyst at an e-commerce company, I helped evaluate a recommendation system. Initially, we focused on metrics like click-through rate (CTR) to measure engagement. However, we soon realized that we needed to consider metrics like user satisfaction and retention. By conducting A/B tests and gathering user feedback, we could understand which recommendations improved the overall user experience. This highlighted for me the importance of context in selecting evaluation metrics, as it directly affected our business outcomes and customer satisfaction.