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About this interview
A technical interview on Classic ML Algorithms, pitched at the medium level. A voice AI interviewer leads the conversation, adapts its questions to your answers, keeps you on topic, and afterward gives you honest, specific feedback on where you were strong and where to improve. Expect roughly 30 minutes.
What you'll be assessed on
Explain how decision trees split nodes and when to stop, including pruning strategies
Describe bagging vs boosting and the key differences between Random Forest, GBDT, XGBoost, and LightGBM
Articulate how XGBoost handles missing values, regularization, and parallelism
Reason about which ensemble method to prefer given dataset size and noise level
Topics covered
Decision Tree SplittingDecision Tree OverfittingDecision Tree PruningDecision Tree StoppingDecision Tree VariantsBaggingRandom ForestBoosting vs BaggingAdaBoostGBDTGBDT HyperparametersRF vs GBDTXGBoostXGBoost Missing Values
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“Can you walk me through how a decision tree actually splits a node? What information does it use to decide which feature and which threshold to split on?
“What is the role of the learning rate in gradient boosting, and how does it interact with the number of trees you train?
“Given a dataset with a million rows, mostly tabular, moderate noise, and a mix of numerical and categorical features, how would you decide between Random Forest, XGBoost, and LightGBM?