Explain KNN and how to tune it

Q: Explain KNN and how to tune it

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.

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Question

K-Nearest Neighbors (KNN) fundamentals

You are interviewing for a Data Scientist role.

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

Explain KNN and how to tune it

K-Nearest Neighbors (KNN) fundamentals

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Explain KNN and how to tune it

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K-Nearest Neighbors (KNN) fundamentals

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