Decision tree learning is a predictive model used for machine learning, statistics, and data mining. It involves answering questions and exploring subcategories to determine the value of an item. The process progresses from top to bottom and can result in categorization or the identification of an object’s class.
Decision tree learning uses a predictive model with tree-like information branches to gather hypotheses and make a judgment about the value of an item. The system is used for machine learning, statistics and data mining. Decision trees are also known as regression or classification trees, depending on the purpose for which they are used.
The decision tree learning process involves moving from one branch of information to another. As each item is reached, whether by computer or person, it must be determined whether or not it applies to the target item. Once each branch has been explored, the answers can be used to determine value.
In essence, decision tree learning is the process of answering questions. Each answer advances the process until there is enough information to make a decision. For example, a simple tree might start by asking which of two items to buy. One question might ask if the item is useful, while another might ask if one item is better priced than the other. By asking all of these questions it is usually possible to determine which stock is statistically more beneficial.
Decision tree learning also explores subcategories. Answering one question can lead to another. This might result in some branches having many sub-branches, while others are less elaborate because the question is easy to answer. By following the process in this way, the user can develop a more detailed evaluation of the item.
Another possible use of decision tree learning is categorization. Rather than each question leading to a single decision, a body of information is broken down into different areas, based on the answer for each branch. Once all branches have been classified, the same process can be performed on each category as well.
Decision tree learning typically progresses from the top level down. He doesn’t tend to backtrack. Once a question has been fully answered, there is usually no need to refer to it again until the results are compiled.
The learning outcomes of the decision tree can be expressed in various ways. They can be the answer to a yes or no question or a number such as a price or time period. The results can also reveal the identity of a certain object and thus name the class to which it belongs.
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