How do you assess the performance of a machine learning model and what metrics would you use for classification versus regression tasks?

In my senior year, I worked on a project to build a classifier that could predict whether an email was spam or not. We used metrics like accuracy to see how many emails were correctly classified. However, we also looked at precision and recall because a high accuracy rate could be misleading if the data was imbalanced. For instance, if 90% of emails were not spam, a model that labeled everything as 'not spam' would still have high accuracy but would fail in practice. By focusing on precision and recall, we were able to refine our model and ensure it performed well even with a skewed dataset.

During my time volunteering for a local NGO, I helped analyze survey data to assess community needs. We were tasked with predicting the level of interest in various programs offered by the NGO. For this, we used regression analysis. I learned to apply metrics like mean absolute error to evaluate how close our predictions were to actual responses. This experience taught me that understanding the performance of our model was crucial in making informed decisions about which programs to prioritize based on community interest.

In my first job as a data analyst at a retail firm, I worked on a project forecasting sales for the upcoming season using regression models. I used R-squared to measure how well our model explained the variability in sales data. We also tracked mean squared error to understand the average difference between predicted and actual sales. This allowed the team to develop more accurate marketing strategies and inventory management plans, demonstrating how critical model evaluation is in driving business decisions.