Decision tree

Decision tree is a type of graphical model used in statistics, data mining, and machine learning. It uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements.

Overview

A decision tree is a flowchart-like structure in which each internal node represents a "test" on an attribute (e.g. whether a coin flip comes up heads or tails), each branch represents the outcome of the test, and each leaf node represents a class label (decision taken after computing all attributes). The paths from root to leaf represent classification rules.

Types of Decision Trees

There are two main types of decision trees: classification tree and regression tree.

• Classification tree analysis is when the predicted outcome is the class (discrete) to which the data belongs.
• Regression tree analysis is when the predicted outcome can be considered a real number (e.g. the price of a house, or a patient's length of stay in a hospital).

Advantages and Disadvantages

Advantages

• Decision trees are simple to understand and interpret.
• Requires little data preparation.
• Able to handle both numerical and categorical data.

Disadvantages

• Decision-tree learners can create over-complex trees that do not generalize the data well.
• Decision trees can be unstable because small variations in the data might result in a completely different tree being generated.

See Also

• Random forest
• Boosting (machine learning)
• Data mining
• Machine learning

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  Manual decision tree
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  Decision tree elements
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  Decision calculations
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  Risk preference sensitivity to threshold
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  Beach decision tree
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  Lifeguards
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  Factory influence diagram
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  Real node
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  Information gain tree
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  Phi function tree