What are some common evaluation metrics used in classification problems, and how do you choose the right one?

In my final year at college, I worked on a project where we developed a classification model to predict student performance based on various factors. We initially used accuracy as our primary evaluation metric. However, when we received an imbalanced dataset where most students performed well, accuracy was misleading. Realizing this, we shifted our focus to precision and recall to ensure that we accurately identified students who might struggle. This shift helped us improve the model's effectiveness, and we ended up presenting a much more robust evaluation during our project defense.

While volunteering for a local charity, I helped classify donation requests based on urgency and type of need. Initially, we measured our success using simple accuracy, but we noticed that many critical requests were being overlooked. I suggested using recall as a metric to ensure we captured as many urgent requests as possible. By doing so, we increased our response rate to critical needs by 30%. This experience taught me the importance of selecting the right metric based on the impact of the decisions being made.

In my first job at a startup, I was part of a team developing a classification model for predicting customer churn. We initially relied on accuracy, but as we analyzed the results, it became clear that we needed a more nuanced approach. We began using the F1 score to balance precision and recall, as our goal was to minimize false positives while ensuring that we identified as many potential churners as possible. This experience reinforced my understanding that the choice of evaluation metric should align with the specific objectives of the business, ensuring we make data-driven decisions.