Why might MCC or Cohen's kappa be safer than accuracy?
PICTURE THIS: HOW TO EXPLAIN IT
Simple meaning
Matthews correlation uses all four confusion-matrix cells and stays near zero for majority-class dummy models.
WHY — Metrics instead of guessing?
Why interviewers care about Metrics:
on Metrics.
the situation, the default choice, and one exception - that reads as experience.
Name the idea, why it exists, then one short example.
End with when you use it and one common pitfall.
STEPS — What happens step by step?
Before you speak the answer, walk the interviewer through these steps:
- 1Matthews correlation uses all
four confusion-matrix cells and stays near zero for majority-class dummy models.
- 2Why it exists
Kappa adjusts agreement for chance.
- 3Both still depend on
a threshold, so they complement PR curves rather than replace them.
- 4Give an example
One tiny concrete case you can say aloud.
- 5Common mistake
What juniors usually get wrong.
- 6Close
When you pick this over the alternative.
EXAMPLE — See it in action
Here's a short line you can speak, broken into clear beats:
Note: Adapt this scaffold to your own project — keep it under 60–90 seconds.
Key takeaway
Matthews correlation uses all four confusion-matrix cells and stays near zero for majority-class dummy models. Kappa adjusts agreement for chance.