Questions & Answers

Instructor: Applied AI Course Duration: 30 mins

Revision Questions:


  1. Explain about K-Nearest Neighbors?(
  2. Failure cases of KNN?(
  3. Define Distance measures: Euclidean(L2) , Manhattan(L1), Minkowski,  Hamming?(
  4. What is Cosine Distance & Cosine Similarity?(
  5. How to measure the effectiveness of k-NN?(
  6. Limitations of KNN?(
  7. How to handle Overfitting and Underfitting in KNN?(
  8. Need for Cross validation?(
  9. What is K-fold cross validation?(
  10. What is Time based splitting?(
  11. Explain k-NN for regression?(
  12. Weighted k-NN ?(
  13. How to build a kd-tree.?(
  14. Find nearest neighbors using kd-tree?(
  15. What is Locality sensitive Hashing (LSH)?(
  16. Hashing vs LSH?(
  17. LSH for cosine similarity?(
  18. LSH for euclidean distance?(

Self Learning:

  1. In k-means or kNN, we use euclidean distance to calculate the distance between nearest neighbours. Why not manhattan distance ?(
  2. How to test and know whether or not we have overfitting problem?(
  3. How is kNN different from k-means clustering?(
  4. Can you explain the difference between a Test Set and a Validation Set?(
  5. How can you avoid overfitting in KNN?(

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