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Algorithm for Decision Tree Induction

  • Basic algorithm (a greedy algorithm)
    • Tree is constructed in a top-down recursive divide-and-conquer manner
    • At start, all the training examples are at the root
    • Attributes are categorical (if continuous-valued, they are discretized in advance)
    • Examples are partitioned recursively based on selected attributes
    • Test attributes are selected on the basis of a heuristic or statistical measure (e.g., information gain)
  • Conditions for stopping partitioning
    • All samples for a given node belong to the same class
    • There are no remaining attributes for further partitioning – majority voting is employed for classifying the leaf
    • There are no samples left

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