Easy Metrics Question 17 of 223

When is accuracy a misleading metric?

AI & Data Analytics · Speak this in 60–90 seconds · Faridabad & Delhi NCR

PICTURE THIS: HOW TO EXPLAIN IT

IdeaMetrics

HowWhat happens inside

Why they askShows real use

Simple meaning

Accuracy looks strong on imbalanced data even if you always predict the majority class.

WHY — Metrics instead of guessing?

Why interviewers care about Metrics:

They are checking judgment

on Metrics.

A good answer names

the situation, the default choice, and one exception - that reads as experience.

Stay structured

Name the idea, why it exists, then one short example.

Close cleanly

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:

1
Accuracy looks strong on
imbalanced data even if you always predict the majority class.
2
It also hides whether
false positives or false negatives are more costly.
3
Prefer precision, recall, F1,
ROC-AUC, PR-AUC, or a business cost.
4
Give an example
One tiny concrete case you can say aloud.
5
Common mistake
What juniors usually get wrong.
6
Close
When you pick this over the alternative.

EXAMPLE — See it in action

Here's a short line you can speak, broken into clear beats:

Say this line

“It also hides whether false positives or false negatives are more costly.”

Break into beats

Italsohideswhetherfalsepositives

Speaking order

2987408337471632900

Note: Adapt this scaffold to your own project — keep it under 60–90 seconds.

Key takeaway

Accuracy looks strong on imbalanced data even if you always predict the majority class. It also hides whether false positives or false negatives are more costly.