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As mentioned in Section 2231, prediction is one of the most important types of data mining We outline what is classi cation, study techniques for building one type of classi ers, called decision tree classi ers, and then study other prediction techniques Abstractly, the classi cation problem is this: Given that items belong to one of several classes, and given past instances (called training instances) of items along with the classes to which they belong, the problem is to predict the class to which a new item belongs The class of the new instance is not known, so other attributes of the instance must be used to predict the class Classi cation can be done by nding rules that partition the given data into disjoint groups For instance, suppose that a credit-card company wants to decide whether or not to give a credit card to an applicant The company has a variety of information about the person, such as her age, educational background, annual income, and current debts, that it can use for making a decision Some of this information could be relevant to the credit worthiness of the applicant, whereas some may not be To make the decision, the company assigns a creditworthiness level of excellent, good, average, or bad to each of a sample set of current customers according to each customer s payment history Then, the company attempts to nd rules that classify its current customers into excellent, good, average, or bad, on the basis of the information about the person, other than the actual payment history (which is unavailable for new customers) Let us consider just two attributes: education level (highest degree earned) and income The rules may be of the following form: person P, P degree = masters and P income > 75, 000 P credit = excellent person P, P degree = bachelors or (P income 25, 000 and P income 75, 000) P credit = good Similar rules would also be present for the other credit worthiness levels (average and bad) The process of building a classi er starts from a sample of data, called a training set For each tuple in the training set, the class to which the tuple belongs is already known For instance, the training set for a credit-card application may be the existing customers, with their credit worthiness determined from their payment history The actual data, or population, may consist of all people, including those who are not existing customers There are several ways of building a classi er, as we shall see. rdlc pdf 417 PDF417 Barcode Creating Library for RDLC Reports | Generate ...
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NET web & IIS applications; Easy to draw & create 2D PDF - 417 barcode images in jpeg, gif, png and bitmap files; Able to generate & print PDF - 417 in RDLC ... We illustrate this with an example EXAMPLE 11-1 Consider the circuit shown in Fig 11-2 Find the two currents i 1 (t) and i 2 (t) if v s (t) = 100 cos 4t The decision tree classi er is a widely used technique for classi cation As the name suggests, decision tree classi ers use a tree; each leaf node has an associated class, and each internal node has a predicate (or more generally, a function) associated with it Figure 226 shows an example of a decision tree To classify a new instance, we start at the root, and traverse the tree to reach a leaf; at an internal node we evaluate the predicate (or function) on the data instance, The McGraw Hill Companies, 2001 rdlc pdf 417 PDF - 417 Client Report RDLC Generator | Using free sample for PDF ...
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Barcode Control SDK supports generating Data Matrix, QR Code, PDF - 417 barcodes in RDLC Local Report using VB and C# class library both in ASP.NET and ... to nd which child to go to The process continues till we reach a leaf node For example, if the degree level of a person is masters, and the persons income is 40K, starting from the root we follow the edge labeled masters, and from there the edge labeled 25K to 75K, to reach a leaf The class at the leaf is good, so we predict that the credit risk of that person is good Building Decision Tree Classi ers The question then is how to build a decision tree classi er, given a set of training instances The most common way of doing so is to use a greedy algorithm, which works recursively, starting at the root and building the tree downward Initially there is only one node, the root, and all training instances are associated with that node At each node, if all, or almost all training instances associated with the node belong to the same class, then the node becomes a leaf node associated with that class Otherwise, a partitioning attribute and partitioning conditions must be selected to create child nodes The data associated with each child node is the set of training instances that satisfy the partitioning condition for that child node In our example, the attribute degree is chosen, and four children, one for each value of degree, are created The conditions for the four children nodes are degree = none, degree = bachelors, degree = masters, and degree = doctorate, respectively The data associated with each child consist of training instances satisfying the condition associated with that child At the node corresponding to masters, the attribute income is chosen, with the range of values partitioned into intervals 0 to 25,000, 25,000 to 50,000, 50,000 to 75,000, and over 75,000 The data associated with each node consist of training instances with the degree attribute being masters, and the income attribute being in each of these ranges,. The McGraw Hill Companies, 2001 The settings that are available if you have decided to create Windows Media files are actually quite similar to those for RealVideo files This shows the Make Windows Media tab of the Pinnacle Studio Setup Options dialog box: 22
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