Interview Q&A / Machine Learning
Machine Learning interview questions
Supervised vs unsupervised learning, overfitting, metrics and the classic models, explained simply.
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- Can you handle class imbalance without changing the data? What is threshold tuning?
Yes. Most classifiers output a probability, and the default cut-off of 0.5 is arbitrary. Lowering it for a rare class raises recall at the cost of precision. Pick the threshold from the precision-recall curve using business costs.
IntermediateQuick answer1 min read - How do class weights, oversampling and undersampling differ for imbalanced data?
Class weights make mistakes on the rare class cost more during training. Oversampling duplicates or synthesises minority rows; undersampling removes majority rows. Weights change no data, oversampling can overfit, undersampling can throw away useful rows.
IntermediateQuick answer1 min read - How does SMOTE work, and what are its limitations?
SMOTE creates synthetic minority samples by picking a minority point, choosing one of its nearest minority neighbours, and placing a new point at a random spot on the line between them. It reduces duplication but can create noisy points near class borders.
IntermediateQuick answer1 min read