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K-Nearest Neighbor

  • July 15, 2023
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KNN also known as:

  • On-demand or Lazy Learning
  • Memory-Based Reasoning
  • Example-Based Reasoning
  • Instance-Based Learning
  • Rote Learning

KNN is effective in both situations, whether Y is continuous or discrete.

KNN is based on measuring the separation between different locations. Any distance measurement, including the Euclidean distance covered in earlier sections, qualifies as distance.

K-Nearest Neighbor

A better variant of KNN assigns more weights to the neighbours in accordance with how far they are from the query location.

When predicting continuous output, the average of all output values will be used, and when predicting categorical output, the majority count of all output values will be used.

K-Nearest Neighbor

Because it is employed to address the problem, the choice of the "K" value is crucial.

Outliers have an impact on low 'K' values.

'K' values that are high might add data points from other categories.

Pros (Advantages) and Cons (Disadvantages)

Strengths Weakness
Does not depend on the underlying data distribution There is no model produced and hence no interesting relationship among output and inputs is learnt
Testing process will be very fast Memory requirement is large because distance calculations is saved in memory
  Testing process is slower in comparison to other models
  Categorical Inputs require additional processing
  Suffers from Curse of dimensionality

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