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What are the methods for expressing attribute test conditions?
Decision tree induction is the learning of decision trees from class-labeled training tuples. A decision tree is a sequential diagram-like tree structure, where every internal node (non-leaf node) indicates a test on an attribute, each branch defines a result of the test, and each leaf node (or terminal node) influences a class label. The largest node in a tree is the root node.
Decision tree induction generates a flowchart-like structure where each internal (non-leaf) node indicates a test on an attribute, each branch corresponds to a result of the test, and each external (leaf) node indicates a class prediction.
At each node, the algorithm selects the “best” attribute to divide the data into single classes. When decision tree induction is used for attribute subset selection, a tree is generated from the given data.
Some attributes that do not occur in the tree are considered to be irrelevant. The set of attributes occurring in the tree forms the decreased subset of attributes. Decision tree induction algorithms support an approach for defining an attribute test condition and its correlating results for multiple attribute types.
Binary Attributes − A binary attribute is a nominal attribute with only two elements or states including 0 or 1, where 0 frequently represents that the attribute is absent, and 1 represents that it is present. Binary attributes are defined as Boolean if the two states are equivalent to true and false.
A binary attribute is symmetric if both of its states are equal valuable and make an equal weight. There is no preference on which results must be coded as 0 or 1. An example can be the attribute gender having the states male and female.
A binary attribute is asymmetric if the outcomes of the states are not equally essential, such as the positive and negative outcomes of a medical check for HIV. By convention, it can code the most essential result, which is generally the nearest one, by 1 (e.g., HIV positive) and the different by 0 (e.g., HIV negative).
Nominal Attributes − Nominal defines associating with names. The values of a nominal attribute are symbols or names of things. Each value defines some type of category, code, or state, etc. Nominal attributes are defined as categorical. The values do not have any significant order. In computer science, the values are also called enumerations.
Ordinal Attributes − An ordinal attribute is an attribute with applicable values that have an essential series or ranking among them, but the magnitude between successive values is unknown.
Ordinal attributes can make binary or multiway splits. Ordinal attribute values can be combined considering the grouping does not violate the order nature of the attribute values.
Numeric Attributes − A numeric attribute is quantitative. It is a computable quantity, represented in numerical or real values. It can be interval-scaled or ratio-scaled.
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