Supervised Learning - Classification Week 8 Challenge

Answer 1: pros:

• Pros:

  • No assumptions about data — useful, for example, for nonlinear data
  • Simple algorithm — to explain and understand/interpret
  • High accuracy (relatively) — it is pretty high but not competitive in comparison to better supervised learning models
  • Versatile — useful for classification or regression
  • it provides a variety of distance criteria to choose from: Euclidean Distance, Manhattan Distance, Minkowski Distance, Hamming Distance

  Cons:

  • Computationally expensive — because the algorithm stores all of the training data
  • High memory requirement
  • Stores all (or almost all) of the training data
  • The prediction stage might be slow (with big N)
  • Sensitive to irrelevant features and the scale of the data

Answer 2: file is attached