Decision tree ING SHEN SSE TONGJLUNIVERSITY OCT.2016
Decision Tree Y I NG SH EN SSE, TO NG JI UNI VERSITY O CT. 2016
Decision tree We can solve a classification problem by asking a series of carefully crafted questions about the attributes of the test record Each time we receive an answer, a follow-up question is asked This process is continued until we reach a conclusion about the class label of the record 1/30/2021 PATTERN RECOGNITION
Decision tree We can solve a classification problem by asking a series of carefully crafted questions about the attributes of the test record. Each time we receive an answer, a follow-up question is asked. This process is continued until we reach a conclusion about the class label of the record. 1/30/2021 PATTERN RECOGNITION 2
Decision tree The series of questions and answers can be organized in the form of a decision tree It is a hierarchical structure consisting of nodes and directed edges. The tree has three types of nodes A root node that has no incoming edges and zero or more outgoing eages Internal nodes, each of which has exactly one incoming edge and two or more outgoing edges Leaf or terminal nodes, each of which has exactly one incoming edge and no outgoing edges 1/30/2021 PATTERN RECOGNITION
Decision tree The series of questions and answers can be organized in the form of a decision tree. It is a hierarchical structure consisting of nodes and directed edges. The tree has three types of nodes ◦ A root node that has no incoming edges, and zero or more outgoing edges. ◦ Internal nodes, each of which has exactly one incoming edge and two or more outgoing edges. ◦ Leaf or terminal nodes, each of which has exactly one incoming edge and no outgoing edges. 1/30/2021 PATTERN RECOGNITION 3
Decision tree In a decision tree, each leaf node is assigned a class label The non-terminal nodes, which include the root and other internal nodes, contain attribute test conditions to separate records that have different characteristics 1/30/2021 PATTERN RECOGNITION
Decision tree In a decision tree, each leaf node is assigned a class label. The non-terminal nodes, which include the root and other internal nodes, contain attribute test conditions to separate records that have different characteristics. 1/30/2021 PATTERN RECOGNITION 4
Decision tree Classifying a test record is straightforward once a decision tree has been constructed Starting from the root node, we apply the test condition We then follow the appropriate branch based on the outcome of the test This will lead us either to Another internal node, for which a new test condition is applied, or A leaf node The class label associated with the leaf node is then assigned to the record 1/30/2021 PATTERN RECOGNITION
Decision tree Classifying a test record is straightforward once a decision tree has been constructed. Starting from the root node, we apply the test condition. We then follow the appropriate branch based on the outcome of the test. This will lead us either to ◦ Another internal node, for which a new test condition is applied, or ◦ A leaf node. The class label associated with the leaf node is then assigned to the record. 1/30/2021 PATTERN RECOGNITION 5