Model Evaluation Metrics
A model is only as good as how you measure it. The wrong metric hides a bad model.
Learn it
Learn accuracy, precision, recall, F1 (for classification) and MAE/RMSE/R² (for regression). Understand why accuracy misleads on imbalanced data.
By the end you’ll be able to
- Choose the right metric
- Compare models fairly
Free resources
- StatQuest — ROC and AUC, Clearly Explained (video)(opens in a new tab)Start here
Confusion matrix, sensitivity, thresholds
Free~16 min - Google — Classification module (metrics)(opens in a new tab)
Accuracy, precision, recall, AUC with exercises
Free~1 hr - scikit-learn — classification_report (reference)(opens in a new tab)
Official API for precision/recall/F1
Free
Practice
For an imbalanced dataset, show how accuracy can be high while recall is poor, and pick a better metric.
Build it — your output
An evaluation report comparing two models on the RIGHT metric for the problem, with a recommendation.
Prove it — check yourself
You’re ready to move on when you can honestly say yes to each:
- Can you choose an appropriate metric?
- Do you know why accuracy misleads on imbalance?
- Can you explain precision vs recall?
Do it with AI
Use a free AI tool as your tutor and pair-programmer. Copy a prompt below into Claude, ChatGPT, or Gemini (for building, try Cursor or Copilot).
The habit: Try it yourself first — a little struggle is where the learning actually happens. Then ask for a hint before the answer — never copy blindly.
- Understand itClaude / ChatGPT / Gemini
I'm a beginner learning "Model Evaluation Metrics". Explain it in simple terms with a real-world analogy, then ask me ONE question to check my understanding — don't reveal the answer.
- PracticeClaude / ChatGPT / Gemini
Give me 3 short practice problems on "Model Evaluation Metrics" (my goal: choose the right metric), from easy to hard. Don't include the solutions yet — I'll try them first, then ask you to check.
- Get unstuckClaude / ChatGPT / Gemini
I'm stuck on this exercise about "Model Evaluation Metrics": [paste your attempt here]. Give me ONE small hint to move forward — do NOT give me the full solution.
- Build & reviewCursor / Copilot
Here is my work for a "Model Evaluation Metrics" task: [paste your code or answer]. Review it: point out what's wrong and WHY, and suggest improvements — but let me make the fixes myself.
- Prove itClaude / ChatGPT / Gemini
Quiz me on "Model Evaluation Metrics". Ask me 5 questions one at a time, wait for my answer each time, then tell me if I'm right and briefly why.