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Quick Look At  Classification
 

Quick Look At Classification

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Quick Look At Classification

Quick Look At Classification

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    Quick Look At  Classification Quick Look At Classification Presentation Transcript

    • Quick Look at CLASSIFICATION
    • Classification
      Each object is assigned to precisely one class
      Naïve Bayes Classifiers
      Uses the probabilistic theory to find the most likely class
      Nearest neighbor classification
      Mainly used when all attribute values are continuous. It is also called as (k-Nearest neighbor or k-NN classification)
    • Basic K – NN Classification Algorithm
      Find ‘k’ training instances that are closest to the unknown instance
      Take the most commonly occurring classification for these ‘k’ instances
      The neighbors can be weighted to improve classification
    • Normalization
      Large magnitudes get more weight while calculating distances and thus nearest neighbors are not properly chosen.
      Normalization ensures that units chosen don’t affect the selection of nearest neighbors
    • Eager & Lazy learning
      Eager learning
      Training data is ‘eagerly’ generalized into some representation model without waiting for unknown instances. Eg. Naïve Bayes algorithm
      Lazy learning
      Training data is not converted to a representation model until an unknown instance is presented for classification. Eg. Nearest neighbor algorithm
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