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Explain KNN and how to tune it

MicrosoftData Scientist · Technical Screen
EasyData ScientistTechnical ScreenMachine Learning

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This question evaluates understanding of the K-Nearest Neighbors algorithm and related competencies such as hyperparameter selection (K, distance metric, weighting), data preprocessing impacts, failure modes in high-dimensional or imbalanced settings, and model evaluation strategies.

K-Nearest Neighbors (KNN) fundamentals

You are interviewing for a Data Scientist role.

  1. Explain how the KNN algorithm works for both classification and regression.
  2. What are the key hyperparameters and design choices?
  • Choice of K
  • Distance metric (e.g., Euclidean, Manhattan, cosine)
  • Weighting (uniform vs distance-weighted neighbors)
  1. What data preprocessing is important for KNN and why? (e.g., feature scaling, handling missing values, categorical encoding)
  2. Discuss the main strengths, weaknesses, and failure modes of KNN.
  • Consider class imbalance, high dimensionality, and large datasets.
  1. How would you select K and evaluate the model? Include at least one approach for avoiding overfitting.

Optionally: Explain how dimensionality reduction (e.g., PCA) could help KNN and when it might hurt.