Decision tree learning

Decision Tree Learning
A decision tree is a structure that allows learning of opinions (e.g. good or bad) about objects based on their attributes (length, colour.).

[edit] Decision tree learning
Main article: Decision tree learning
Decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the item's target value.

Decision tree learning is also a common method used in data mining. Here, a decision tree describes a tree structure wherein leaves represent classifications and branches represent conjunctions of features that lead to those classifications [1].

Decision Tree Learning from The "AIxploratorium" web site. A great introductory tutorial that even includes a demo...

The Hoeffding tree algorithm is a decision tree learning method for stream data classification.
It was initially used to track Web clickstreams and construct models to predict
which Web hosts and Web sites a user is likely to access.

Nevertheless, understanding the basics of decision tree learning thoroughly is imperative - we all must start somewhere. Again, for inquistive readers, check this page for some more advanced decision tree Prolog code.

See also: Machine learning, Decision Trees, Classification, Artificial intelligence, Branch