Moderate Decision Trees Question 98 of 223

How do ID3, C4.5, and CART differ at a high level?

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

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ID3 uses information gain and likes categorical splits.

Why interviewers care about Decision Trees:

This is a process

question about Decision Trees.

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.

Before you speak the answer, walk the interviewer through these steps:

1
ID3 uses information gain
and likes categorical splits.
2
C4.5 adds gain ratio,
missing-value handling, and pruning.
3
CART uses Gini or
squared error, binary splits, and is the family behind sklearn DecisionTree.
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.

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

Say this line

“C4.5 adds gain ratio, missing-value handling, and pruning.”

Break into beats

C45addsgainratiomissing

Speaking order

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

ID3 uses information gain and likes categorical splits. C4.5 adds gain ratio, missing-value handling, and pruning.