What are some common evaluation metrics for classification models, and how do you choose the appropriate one?

During my final year, I worked on a sentiment analysis project for a social media platform. I built a classification model to determine whether posts were positive, negative, or neutral. Initially, I focused on accuracy as my primary evaluation metric, thinking it was the best way to assess performance. However, I soon realized that many posts were neutral, leading to high accuracy but poor model effectiveness. I then explored precision and recall, particularly for the positive sentiment class, to better understand the model's ability to identify truly positive posts. This experience taught me that choosing the right metric depends on the specific goals of the project and the data characteristics.

During my internship at a marketing firm, I was tasked with evaluating a customer churn prediction model. The team had initially considered accuracy as the key metric; however, I suggested using the F1-score instead. Many customers who churned were from a small segment, which meant accuracy could be misleading. By focusing on the F1-score, we were able to balance precision and recall, providing a more nuanced view of how well the model predicted churners. This experience highlighted the importance of understanding the business context and the implications of misclassification when selecting evaluation metrics.

In my first job as a data analyst, I worked on developing a fraud detection system for a financial institution. The team initially relied on accuracy to evaluate the model. However, due to the nature of fraud, where fraudulent transactions were significantly fewer than legitimate ones, we quickly learned that accuracy was not sufficient. We shifted our focus to the ROC-AUC metric, which helped us understand the trade-off between true positive and false positive rates. This approach ultimately led to a more effective model that better balanced the risks involved in fraud detection.