Decision Trees:
Decision Trees (DT) are trees that
classify instances by sorting them based on feature values.
Each node in a decision tree
represents a feature in an instance to be classified, and each branch
represents a value that the node can assume.
Instances are classified starting at the root
node and sorted based on their feature values.
Decision tree learning, used in
data mining and machine learning, uses a decision tree as a predictive model
which maps observations about an item to conclusions about the item's target
value.
More descriptive names for such tree models
are classification trees or regression trees.
Decision tree classifiers usually
employ post-pruning techniques that evaluate the performance of decision trees,
as they are pruned by using a validation set.
Any node can be removed and
assigned the most common class of the training instances that are sorted to it.
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