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This is a Medium difficulty Coding & Algorithms question, commonly asked during Onsite rounds at Other.

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This question is commonly asked for Data Scientist candidates at Other during technical interviews.

Implement KNN from scratch

Last updated: Mar 29, 2026

Quick Overview

This question evaluates understanding of the k-Nearest Neighbors algorithm, distance metrics (Euclidean and cosine), tie-breaking with class priors, weighted voting, complexity analysis, and scalability strategies, testing competencies in algorithm implementation, model behavior, and computational trade-offs.

Other

Oct 13, 2025, 9:49 PM

Data Scientist

Onsite

Coding & Algorithms

Without using ML libraries, implement k-Nearest Neighbors for classification. Requirements: (a) Support Euclidean and cosine distances; (b) Allow tie-breaking via class priors; (c) Analyze training time, query time, and memory complexity; (d) Extend to weighted voting with distance decay; (e) Discuss how you would scale queries to millions of points (e.g., KD-tree limitations in high dimensions, approximate methods).

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