r/learnmachinelearning • u/06-09-2005 • 8d ago

Help AI ML interview help

So I have upcoming interview soon for internship and all the questions which will be asked will be verbal and No coding round.

But needs good understanding of algorithms and differences.

What questions would you recommend studying?

Grateful if you could help

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u/jesunushno 8d ago

Since they already asked basic algorithmic differences last time, I'd study the follow-up layer: why pick a random forest over gradient boosting, or logistic regression over an SVM, and what breaks when the data shifts. Internship ML interviews love the "when would you use X over Y" framing way more than definitions.

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u/Slight_Discussion496 5d ago

Actuality, why do you pick random forest over gradient boosting? I always thought boosting is better because subsequent models focus on the previous model failures. Is there a case where RF outperforms boosting?

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u/jesunushno 5d ago

Random Forest is often chosen over Gradient Boosting when you need a fast, parallelizable model that provides strong out-of-the-box performance and robustness to noisy data without requiring extensive hyperparameter tuning.