KNN Algorithm: Master K-Nearest Neighbors for ML (20 FAQs)👴

1. What is KNN? 2. How does KNN work? 3. Distance Metrics 4. Choosing K 5. Classification 6. Regression 7. Optimal K 8. Advantages 9. Disadvantages 10. Lazy Algorithm 11. KNN vs KMeans 12. Normalization 13. High Dimensions 14. Scaling 15. Real-time Usage 16. Weighted KNN 17. Imbalanced Data 18. Time Complexity 19. KNN in Python 20. Applications

No.	Question	Answer
1	What is K-Nearest Neighbors (KNN)?	KNN is a supervised machine learning algorithm used for classification and regression.
2	How does KNN work?	KNN works by storing training data and predicting the class of a new point based on its nearest neighbors using distance metrics.
3	Which distance metrics are used in KNN?	Common metrics: Euclidean, Manhattan, Minkowski, Cosine.
4	How to choose the value of K?	Use cross-validation and pick K that balances bias vs variance.
5	How is KNN used for classification?	New data points are assigned the majority class among neighbors.
6	How is KNN used for regression?	Prediction is based on the average value of neighbors.
7	How do we find the optimal K value?	Use cross-validation. Small K → overfitting. Large K → underfitting.
8	What are the advantages of KNN?	Easy to implement, non-parametric, works for both classification & regression.
9	What are the disadvantages of KNN?	Slow prediction with large datasets, sensitive to irrelevant features.
10	Why is KNN called a lazy learning algorithm?	Because it stores data and delays learning until prediction.
11	What is the difference between KNN and KMeans?	KNN: Supervised, classification. KMeans: Unsupervised, clustering.
12	Why is normalization important in KNN?	Because features with larger ranges can dominate distance calculations.
13	What happens in high dimensions?	Curse of dimensionality: Distance metrics lose effectiveness.
14	Why is feature scaling needed in KNN?	To ensure all features contribute equally to distance.
15	Can KNN be used in real-time systems?	Yes, but optimization (KD-trees, Ball-trees) is required.
16	What is Weighted KNN?	Neighbors are weighted by distance → closer points have higher influence.
17	How does KNN handle imbalanced data?	Use resampling or weighted voting.
18	What is the time complexity of KNN?	Training: O(1). Prediction: O(n × d).
19	How is KNN implemented in Python?	Using scikit-learn: KNeighborsClassifier(), KNeighborsRegressor().
20	What are the applications of KNN?	Recommendation systems, image recognition, medical diagnosis, anomaly detection.