Quick Look At Classification

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Quick Look At Classification

Quick Look At Classification

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  • 1. Quick Look at CLASSIFICATION
  • 2. 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)
  • 3. 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
  • 4. 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
  • 5. 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
  • 6. Visit more self help tutorials
    Pick a tutorial of your choice and browse through it at your own pace.
    The tutorials section is free, self-guiding and will not involve any additional support.
    Visit us at www.dataminingtools.net