What are some common evaluation metrics for classification models, and how do you choose the right one for your use case?

In my final year of college, I worked on a project to predict student performance based on historical data. We used classifiers and evaluated them using various metrics. Initially, we focused on accuracy, but soon realized that it was misleading as our dataset had many students who performed poorly. Therefore, we shifted to using precision and recall to better understand how well our model predicted high-performing students. This experience taught me the importance of context in choosing evaluation metrics, as we aimed to ensure that we could identify students at risk of failing, which required a focus on recall.

During my time volunteering for a non-profit organization, I helped analyze donor data to classify them into different categories based on their giving patterns. We faced a challenge with class imbalance, where one donor type was significantly less represented than others. To gauge our model's performance accurately, we prioritized the F1 score, which balances precision and recall. This was crucial because we wanted to ensure that we were not misclassifying potential large donors. This experience highlighted how understanding the specific needs of a project can guide the choice of evaluation metrics.

In my first job as a data analyst, I worked on a project to predict customer churn for a subscription service. We initially considered accuracy as our main metric, but soon realized that it did not reflect the model's effectiveness in identifying at-risk customers. Instead, we adopted ROC-AUC to understand the trade-offs between true positive and false positive rates. This allowed us to better target our retention efforts, ultimately leading to a significant reduction in churn. This experience reinforced the need to choose metrics based on the specific business goals and the consequences of different types of errors.