Explain KNN and PCA and key tradeoffs
Quick Overview
Evaluates understanding of K-Nearest Neighbors (instance-based classification/regression) and Principal Component Analysis (linear dimensionality reduction), highlighting tradeoffs such as distance metric and preprocessing effects, computational and high-dimensional limitations, PCA’s optimization/computation approaches, and the impact of dimensionality reduction on downstream nonparametric methods. Common in the Machine Learning domain for Data Scientist internships at a fundamentals-to-intermediate abstraction level because it probes both theoretical foundations and practical considerations for applying non-parametric algorithms and linear feature extraction to real datasets.
In a Data Scientist internship interview, you are asked ML fundamentals:
- K-Nearest Neighbors (KNN)
- Explain how KNN works for classification and regression.
- How do you choose k ? What happens when k is too small or too large?
- How do you choose a distance metric (Euclidean, cosine, etc.)?
- What preprocessing is important (feature scaling, handling categorical features)?
- Discuss computational complexity and how you would make KNN work for large datasets.
- What issues arise in high-dimensional spaces (curse of dimensionality)?
- Principal Component Analysis (PCA)
- What optimization problem does PCA solve? Explain the geometric intuition.
- How is PCA computed (covariance eigendecomposition vs SVD)?
- How do you choose the number of components (explained variance, CV)?
- When can PCA hurt performance? (interpretability, non-linear structure, leakage)
- If you apply PCA before KNN, when might it help and when might it hurt?
Provide clear, interview-style answers with practical considerations.