1. Decision tree induction is used for data mining but can result in overfitting due to noise in the training data. Tree pruning methods address this by removing the least reliable branches using statistical measures. 2. There are two tree pruning approaches - prepruning which halts tree construction early, and postpruning which removes subtrees from a fully grown tree. Cost complexity pruning removes subtrees if they increase encoding costs. 3. For large datasets, decision tree construction becomes inefficient due to memory swapping. More scalable algorithms like BOAT use bootstrapping statistics to construct trees from samples rather than requiring the full dataset to fit in memory.

1. Data Mining
Decision Tree Induction
By,
Dharshini N
MCA-I
20PMC03

2. Tree Pruning
O A decision tree is build many of the branches
will reflect anomalies in the training data due
to noise or outlier.
O Tree pruning methods address this problem of
overfitting the data.
O Such methods typically use statistical measure
to remove the least reliable branches.

3. O In the prepruning approach a tree is “pruned” by halting
its construction early.
O The second and more common approach is Postpruning
which removes subtrees from a “fully grown ”tree.
O A subtree at a given node is pruned by removing its
branches and replacing it with a leaf.
O The cost complexity pruning algorithm used in CART
is an example of the postprunning approach.

4. O A pruning set of class-labeled tuples is used to estimate
cost complexity.
O This set is independent of the training set used to build
the unpruned tree and of any test set used for accuracy
estimation.
O A method called Pessimistic pruning ,which is similar to
the cost complexity is preferred.
O The best pruned tree is the one that minimizes the number
of encoding bits.

8. Scalability and Decision Tree
Induction
O In data mining applications very large training
sets of millions of tuples are common.
O Decision tree construction becomes inefficient
due to swapping of the training tuples in and
out of main and cache memories.
O More scalable approaches, capable of handling
training data that are too large to fit in
memory , required.

9. O BOAT(Bootstrapped Optimistic Algorithm For Tree
Construction) is a decision tree algorithm that takes a
completely different approach to scalability.
O It is not based on the use of any special data structures.
O Instead, it uses a statistical technique known as boost
strapping.

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