What are some common metrics used to evaluate the performance of a regression model?

During my final year in college, I worked on a project analyzing house prices using a regression model. We utilized metrics like Mean Absolute Error (MAE) and R-squared to evaluate our model's performance. MAE helped us understand the average error in our predictions, which was crucial for our stakeholders looking to buy houses within a specific budget. Meanwhile, R-squared gave us insights into how well our model explained the variability in house prices. By presenting these metrics, we effectively communicated the strengths and limitations of our model to our professors and peers, making our findings more impactful.

In my role as a volunteer for a non-profit organization, I participated in a project to predict fundraising outcomes for our events. We implemented a regression model and evaluated it using metrics like Root Mean Squared Error (RMSE) to determine how well our predictions aligned with actual outcomes. This was particularly important for planning future events and setting realistic fundraising goals. We learned that using RMSE helped us focus on larger errors, which was critical since large discrepancies could significantly affect our planning. This experience taught me the importance of selecting appropriate evaluation metrics based on project goals.

During my internship at a retail company, I helped develop a sales forecasting model. We used various metrics to evaluate its performance, including Mean Squared Error (MSE) and R-squared. MSE was particularly useful as it penalized larger errors more heavily, which was important because our team wanted to avoid significant miscalculations in inventory levels. R-squared helped us understand how much of the sales variability was explained by our model. This experience reinforced my understanding of how different metrics can provide insights into model performance and how to communicate these effectively to stakeholders.