Current Slide

Small screen detected. You are viewing the mobile version of SlideWiki. If you wish to edit slides you will need to use a larger device.

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

Speaker notes:

Content Tools


There are currently no sources for this slide.