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Tree pruning

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Tree pruning is useful for the removal of overfitting...

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 Tree pruning is performed in order to remove overfitting in the training data due to noise or outliers.  The pruned trees are smaller and less complex.  Two strategies for “pruning” the decision tree:  Postpruning - take a fully-grown decision tree and discard unreliable parts(sub trees)  Prepruning - stop growing a branch when information becomes unreliable
 The efficiency of existing decision tree algorithms has been well established for relatively small data sets.  Efficiency becomes an issue of concern when these algorithms are applied to the mining of very large real-world databases.  As in the data mining application, millions of tuples are common in the data set. Which needs to fit in the memory(As per the decision tree restriction)
 The AVC-set of an attribute A at node N gives the class label counts for each value of A for the tuples at N.  The set of all AVC-sets at a node N is the AVC-group of N.  The size of an AVC-set for attribute A at node N depends only on the number of distinct values of A and the number of classes in the set of tuples at N.  Typically, this size should fit in memory, even for real-world data.
 It uses a statistical technique known as “bootstrapping” to create several smaller samples (or subsets) of the given training data, each of which fits in memory.  Each subset is used to construct a tree, resulting in several trees. The trees are examined and used to construct a new tree,  T`, that turns out to be “very close” to the tree that would have been generated if all the original training data had fit in memory.
 Faster in constructing tree  Insertion and deletion is easy without constructing the tree from scratch
 (PBC) Perception based classification is an interactive approach based on multidimensional visualization techniques.  It allows the user to incorporate background knowledge about the data when building a decision tree.  By visually interacting with the data, the user is also likely to develop a deeper understanding of the data.  The resulting trees tend to be smaller than those built using traditional decision tree induction methods and so are easier to interpret, while achieving about the same accuracy.
Tree pruning

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Tree pruning

  • 2.  Tree pruning is performed in order to remove overfitting in the training data due to noise or outliers.  The pruned trees are smaller and less complex.  Two strategies for “pruning” the decision tree:  Postpruning - take a fully-grown decision tree and discard unreliable parts(sub trees)  Prepruning - stop growing a branch when information becomes unreliable
  • 3.
  • 4.  The efficiency of existing decision tree algorithms has been well established for relatively small data sets.  Efficiency becomes an issue of concern when these algorithms are applied to the mining of very large real-world databases.  As in the data mining application, millions of tuples are common in the data set. Which needs to fit in the memory(As per the decision tree restriction)
  • 5.  The AVC-set of an attribute A at node N gives the class label counts for each value of A for the tuples at N.  The set of all AVC-sets at a node N is the AVC-group of N.  The size of an AVC-set for attribute A at node N depends only on the number of distinct values of A and the number of classes in the set of tuples at N.  Typically, this size should fit in memory, even for real-world data.
  • 6.
  • 7.  It uses a statistical technique known as “bootstrapping” to create several smaller samples (or subsets) of the given training data, each of which fits in memory.  Each subset is used to construct a tree, resulting in several trees. The trees are examined and used to construct a new tree,  T`, that turns out to be “very close” to the tree that would have been generated if all the original training data had fit in memory.
  • 8.  Faster in constructing tree  Insertion and deletion is easy without constructing the tree from scratch
  • 9.
  • 10.  (PBC) Perception based classification is an interactive approach based on multidimensional visualization techniques.  It allows the user to incorporate background knowledge about the data when building a decision tree.  By visually interacting with the data, the user is also likely to develop a deeper understanding of the data.  The resulting trees tend to be smaller than those built using traditional decision tree induction methods and so are easier to interpret, while achieving about the same accuracy.