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Decision Trees
DECISION TREE
An internal node is a test on an attribute.
A branch represents an outcome of the test, e.g., Color=red.
A leaf node represents a class label or class label distribution.
At each node, one attribute is chosen to split training examples into distinct classes as much as possible
A new case is classified by following a matching path to a leaf node.
Posts: 2,300
Threads: 878
Joined: Sep 2010
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Decision Trees
Basics of Decision Trees
A flow-chart-like hierarchical tree structure
Often restricted to a binary structure
Root: represents the entire dataset
A node without children is called a leaf node. Otherwise is called an internal node.
Internal nodes: denote a test on an attribute
Branch (split): represents an outcome of the test
Univariate split (based on a single attribute)
Multivariate split
Leaf nodes: represent class labels or class distribution
Most decision tree generation consists of two phases
Tree construction
Tree pruning
