Moderate PCA Question 118 of 223

How do you choose the number of principal components?

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

PICTURE THIS: HOW TO EXPLAIN IT

IdeaPCA

HowWhat happens inside

Why they askShows real use

Simple meaning

Look at the explained-variance ratio and keep components until a target such as 90 or 95 percent is reached.

WHY — PCA instead of guessing?

Why interviewers care about PCA:

This is a process

question about PCA.

Panels listen for order,

trade-offs, and what you would actually do on a AI / ML project - not buzzwords.

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
Look at the explained-variance
ratio and keep components until a target such as 90 or 95 percent is reached.
2
A scree plot elbow
is a second heuristic.
3
For a downstream model,
treat n_components as a hyperparameter and pick it with cross-validation.
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

“A scree plot elbow is a second heuristic.”

Break into beats

Ascreeplotelbowisa

Speaking order

2987408337471632900

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

Key takeaway

Look at the explained-variance ratio and keep components until a target such as 90 or 95 percent is reached. A scree plot elbow is a second heuristic.