Decision Tree is one of the popular machine learning algorithms which is more powerful for classification and regression problems. The main reason behind its popularity is that it imitates the level according to which human thinks, hence makes easier to understand. In a decision tree each node represents an attribute, each link represents a rule and each leaf represents an outcome.
According to the description of the data, decision tree divides the given set of data into smaller data sets so that we get a set of data points that can be categorized in a specific set. This method uses number of algorithms to
build a decision tree.
ID3
Developed by Ross Quinlan, it is an algorithm used to develop a decision tree from a given number of the data. The tree thus developed helps in making a decision.
C4.5
It was also developed by Ross Quinlan, and also known as an extended form of ID3, thus invented to develop a decision tree. The decision tree thus generated can be used for classification so it’s often named as a statistical classifier.
C5.0
It is an extended form of C4.5 and similar to the previous version C4.5 it can be often used for classification. The only difference lies in the size of the tree and computation time.
J4.8
J4.8 is simply a C4.5 algorithm for generating a decision tree which can further be used in classification.