What are some common metrics you would use to evaluate the performance of a classification model, and why are they important?

In my final year project, I built a classification model to predict student performance based on various factors like attendance and assignment scores. I primarily used accuracy to get a general sense of how well my model performed. However, I also discovered the importance of precision and recall, especially when identifying students at risk of failing. For instance, during testing, my model showed an accuracy of 85%, but it only had a recall of 60%. This meant I was missing many students who needed help. It taught me that in educational settings, identifying the right students is more critical than just having a high accuracy rate.

While volunteering for a non-profit, I helped analyze the effectiveness of a fundraising campaign. We built a basic classification model to predict whether a donor would contribute based on their previous donation behaviors. We focused on precision to minimize the number of false positives, ensuring that we only reached out to likely donors. This approach saved time and resources, allowing us to increase our actual contributions by 20%. This experience highlighted how understanding metrics like precision can directly influence decision-making in real-world scenarios.

During my internship at a tech company, I was involved in a project to predict customer churn using a classification model. We used a combination of accuracy, precision, and F1-score to evaluate our model. Initially, we were pleased with an overall accuracy of 90%, but further analysis revealed that our precision was low, meaning we were misclassifying many customers who would not churn. By adjusting our model and focusing on improving the F1-score, we were able to create a more balanced approach, ultimately helping the company retain more customers. This experience underscored the importance of choosing the right metrics based on the business context.