Title: Discussion 11 — Picking the Right Metric
Course: AIINFRA 100: AI Systems & Development Environments > Section: Week 11 — Machine Learning Libraries: scikit-learn

💬 Discussion 11 — Picking the Right Metric (20 pts)

Imagine you built a model this week to predict something you actually care about — it could be the Iris species from the lab, or something from your own life or work (e.g., predicting whether an email is spam, whether a loan should be approved, or whether a house price estimate is accurate). In your post, answer:

- Is your example a classification or regression problem, and why?
- Which metric(s) from this week (accuracy, precision/recall, F1, confusion matrix, MAE, MSE, R²) would you use to judge whether the model is doing a good job — and why is that metric more meaningful than just "accuracy" or "it looks right" in your scenario?
- What's one way accidentally training and testing on the same data could mislead you in this scenario?

Post your initial response, then respond to at least two classmates — do you agree with their choice of metric, or would you pick a different one for their scenario?