What are some common metrics used for evaluating classification models, and when would you use each one?

In my final year of college, I worked on a project to build a spam email classifier. We used a dataset of emails labeled as 'spam' or 'not spam.' For evaluation, we primarily focused on precision and recall. Precision was crucial because we wanted to minimize false positives; we didn’t want legitimate emails marked as spam. Recall was equally important to ensure we captured as many spam emails as possible. After training our model, we achieved a precision of 90% and a recall of 85%. This experience taught me how to choose metrics based on project goals and the implications of misclassifications.

While volunteering for a health awareness campaign, I helped analyze survey data to categorize responses into 'high risk' and 'low risk' for health issues. We used accuracy to evaluate our classification model, as our dataset was balanced. However, I learned that in scenarios where the cost of misclassifying high-risk individuals could lead to severe consequences, metrics like recall should be prioritized. For instance, ensuring we correctly identify those at high risk was more crucial than simply having a high overall accuracy. This experience highlighted the importance of selecting the right evaluation metric based on the context of real-world applications.

In my first job as a data analyst, I worked on a project focused on predicting customer churn for a subscription service. We had a large dataset with class imbalance, as only a small percentage of customers churned. In this case, accuracy alone was insufficient. We opted to use the F1 score, which balances precision and recall, to gauge our model's effectiveness. After tuning our model, we improved the F1 score from 0.65 to 0.80, which helped the company take proactive measures to retain customers. This experience reinforced the importance of understanding model metrics for practical implications in business strategies.