K-Nearest Neighbors

Intuition behind the algorithm :

How do we Decide Value of K?

1.Training error Rate 2.Validation Error Rate

Which value is the nearest value i.e which distance metrics can be used?

Applications of KNN :

  • If you’re searching for semantically similar documents (i.e., documents containing similar topics), this is referred to as Concept Search.
  • The biggest use case of K-NN search might be Recommender Systems. If you know a user likes a particular item, then you can recommend similar items for them.

Advantages :

  • 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

Disadvantages :

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

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Learning how machines learn!!

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Srishti Sawla

Srishti Sawla

Learning how machines learn!!

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