Machine Learning

  UNIT 5 (A) Dimensionality Reduction 1.      The Curse of Dimensionality 2.      Main Approaches for Dimensionality Reduction 3.      Projection 4.      PCA -------------------------------------------------------------------------------------------------------------------------------- Long Answer Questions 1.      What is the curse of dimensionality? 2.      What are the main motivations for reducing a dataset’s dimensionality? What are the main drawbacks? 3.      Explain Projection with example. 4.      Explain PCA with example. Short Answer Questions 1.      Define Dimensionality Reduction. 2.      List main approaches to dimensionality reduction. 3.      List the most popular dimensionality reduction techniques.

                                                                                                   Unit V (A) Dimensionality Reduction 1.       The Curse of Dimensionality 2.       Main Approaches for Dimensionality Reduction 3.       Projection 4.       PCA

                                                                                                      Unit V Dimensionality Reduction 1.      The Curse of Dimensionality 2.      Main Approaches for Dimensionality Reduction 3.      Projection 4.      PCA

 Machine Learning MCQs - 4  (Clustering, Dimensionality Reduction) --------------------------------------------------------------------- 1.  Which of the following is finally produced by Hierarchical Clustering? final estimate of cluster centroids tree showing how close things are to each other assignment of each point to clusters all of the mentioned Ans: 2 2.  Which of the following is required by K-means clustering? defined distance metric number of clusters initial guess as to cluster centroids all of the mentioned Ans: 4 3.  Point out the wrong statement. k-means clustering is a method of vector quantization k-means clustering aims to partition n observations into k clusters k-nearest neighbor is same as k-means none of the mentioned Ans: 3 4.  Which of the following combination is incorrect? Continuous – euclidean distance Continuous – correlation similarity Binary – manhattan distance None of the mentioned Ans: 4 5.  Hierarchical clustering should be primarily used for explorati