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INTRODUCTION TO KNOWLEDGE DISCOVERY AND DATA MINING - CHAPTER 3

Data Mining with Decision Trees - Cây quyết định là những công cụ mạnh mẽ và phổ biến để phân loại và dự báo. Hấp dẫn của phương pháp dựa trên cây là do một phần lớn vào thực tế rằng, trái ngược với các mạng thần kinh, cây quyết định đại diện cho quy tắc. Nội quy có thể dễ dàng được thể hiện để con người chúng ta có thể hiểu họ hoặc trong một ngôn ngữ truy cập cơ sở dữ liệu như SQL để ghi lại rơi vào một thể loại cụ thể có thể. | Chapter 3 Data Mining with Decision Trees Decision trees are powerful and popular tools for classification and prediction. The attractiveness of tree-based methods is due in large part to the fact that in contrast to neural networks decision trees represent rules. Rules can readily be expressed so that we humans can understand them or in a database access language like SQL so that records falling into a particular category may be retrieved. In some applications the accuracy of a classification or prediction is the only thing that matters if a direct mail firm obtains a model that can accurately predict which members of a prospect pool are most likely to respond to a certain solicitation they may not care how or why the model works. In other situations the ability to explain the reason for a decision is crucial. In health insurance underwriting for example there are legal prohibitions against discrimination based on certain variables. An insurance company could find itself in the position of having to demonstrate to the satisfaction of a court of law that it has not used illegal discriminatory practices in granting or denying coverage. There are a variety of algorithms for building decision trees that share the desirable trait of explicability. Most notably are two methods and systems CART and C4.5 See5 C5.0 that are gaining popularity and are now available as commercial software. 3.1 How a decision tree works Decision tree is a classifier in the form of a tree structure where each node is either a leaf node indicating a class of instances or a decision node that specifies some test to be carried out on a single attribute value with one branch and sub-tree for each possible outcome of the test. A decision tree can be used to classify an instance by starting at the root of the tree and moving through it until a leaf node which provides the classification of the instance. Example Decision making in the London stock market Suppose that the major factors affecting the .