Explain KNN and how to tune it

K-Nearest Neighbors (KNN) core concepts

This question is for a Data Scientist interview.

1. Describe how the KNN algorithm works for both classification and regression.
2. Which hyperparameters and design decisions matter most?
   • The value of K
   • Distance metric (for example, Euclidean, Manhattan, cosine)
   • Weighting scheme (uniform versus distance-weighted neighbors)
3. Which data preprocessing steps are important for KNN, and why? (for example, feature scaling, missing value treatment, categorical encoding)
4. Discuss KNN's main strengths, weaknesses, and failure modes.
   • Think about class imbalance, high dimensionality, and large datasets.
5. How would you choose K and assess the model? Include at least one method for preventing overfitting. Optional: Explain how dimensionality reduction (such as PCA) could benefit KNN and when it might be harmful. Overview: This question tests knowledge of the K-Nearest Neighbors algorithm along with related skills: hyperparameter selection (K, distance metric, weighting), the effect of data preprocessing, failure modes in high-dimensional or imbalanced settings, and model evaluation strategies.