Introducation classification of tress in data Mining

      Introducation classification of tress in Data Mining

In this Article today learn classification of tree  Introduction

  Classification by Decision Tree Induction:

 • Decision tree induction is the learning of decision trees from class-labeled training tuples. A decision tree is a flowchart-like tree structure, where each internal node denotes a test on an attribute.  Each branch represents an outcome of the test. Each leaf node holds a class label.  The topmost node in a tree is the root node.

The construction of decision tree classifiers does not require any domain knowledge or parameter setting, and therefore I appropriate for exploratory knowledge discovery Decision trees can handle high dimensional data.

Their representation of acquired knowledge in tree forms intuitive and generally easy to assimilate by humans

The learning and classification steps of decision trees induction are simple and fast. In general, decision tree classifiers have good accuracy.

Decision tree induction algorithms have been used for classification in many application areas, such as medical manufacturing and production, financial analysis, astronomy, and molecular biology.