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Learn Knn Data Science Tutorial, validate concepts with Knn Data Science MCQ Questions, and prepare interviews through Knn Data Science Interview Questions and Answers.
K-Nearest Neighbors Q&A
1What is KNN?
Answer: Instance-based algorithm predicting from nearby data points.
2How KNN classification works?
Answer: Majority vote among k nearest neighbors.
3How KNN regression works?
Answer: Average target of nearest neighbors.
4Choosing k?
Answer: Tune with cross-validation; small k overfits, large k underfits.
5Distance metrics?
Answer: Euclidean, Manhattan, Minkowski, cosine (task-dependent).
6Need feature scaling?
Answer: Yes, to ensure fair distance computation across features.
7What are weighted neighbors?
Answer: Closer neighbors get higher influence in prediction.
8KNN training time?
Answer: Minimal training; prediction can be expensive.
9How speed up KNN?
Answer: KD-tree/Ball-tree, dimensionality reduction, approximate NN methods.
10KNN limitations?
Answer: Sensitive to noise, irrelevant features, and high dimensionality.
11When KNN works well?
Answer: Small-medium clean datasets with meaningful local structure.
12One-line summary?
Answer: KNN is simple and intuitive but demands good scaling and feature quality.