What are common metrics used to evaluate the performance of regression models, and how do they differ from those used for classification models?

During my final year in college, I worked on a project where we developed a regression model to predict housing prices in our local area. We used metrics like Mean Absolute Error (MAE) and R-squared to evaluate our model's performance. For instance, we found that our model achieved an MAE of $5,000, meaning that on average, our predictions were off by this amount. This hands-on experience helped me understand how important it is to choose the right metrics to assess the model's accuracy and how each metric offers different insights into our model's performance.

In my volunteer role with a local non-profit, I helped analyze survey data to understand community needs. I used regression analysis to see how factors like income and education level influenced access to resources. We evaluated our model using R-squared to see how well it explained the variance in our data. This experience taught me how to apply regression metrics in real-world scenarios and the importance of communicating these findings to stakeholders who were not familiar with technical jargon.

In my first job as a data analyst, I worked on a project where we needed to predict customer churn using both regression and classification models. For the regression part, we relied on Mean Squared Error to measure our model's accuracy. For the classification aspect, we used accuracy and F1-score. This dual experience reinforced my understanding of how different metrics serve different types of models and the necessity of selecting the appropriate ones to drive insights and business decisions.