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Decision Trees is the most popular DM algorithm, used to predict discrete and continuous variables. The algorithm uses the discrete input variables to split the tree into nodes in such a way that each node is more pure in terms of target variable each split leads to nodes where a single state of a target variable is represented better than other states. For continuous predictable variables, you get a piecewise multiple linear regression formula with a separate formula in each node of a tree. A tree that predicts continuous variables is a Regression Tree. Linear Regression predicts continuous variables, using a single multiple linear regression formula. The input variables must be continuous as well. Linear Regression is a simple case of a Regression Tree, a tree with no splits. As Linear Regression is a simple Regression Tree, a Logistic Regression is a Neural Network without any hidden layers. The Na ve Bayes algorithm calculates probabilities for each possible state of the input attribute for every single state of predictable variable. These probabilities are used to predict the target attribute based on the known input attributes of new cases. The Na ve Bayes algorithm is quite simple; it builds the models quickly. Therefore, it s suitable as a starting point in your prediction project. The Na ve Bayes algorithm doesn t support continuous attributes. The Neural Network algorithm is often associated with artificial intelligence. You can use this algorithm for predictions as well. Neural networks search for nonlinear functional dependencies by performing nonlinear transformations on the data in layers, from the input layer through hidden layers to the output layer. Because of the multiple nonlinear transformations, neural networks are harder to interpret compared to Decision Trees. Sequence Clustering searches for clusters based on a model, and not on similarity of cases as Clustering does. The models are defined on sequences of events by using Markov chains. Typical usage of Sequence Clustering would be an analysis of your company s website usage, although you can use this algorithm on any sequential data. You can use the Time Series algorithm to forecast continuous variables. Internally, the Time Series uses two different algorithms. For short-term forecasting, the Auto-Regression Trees (ART) algorithm is used. For long-term prediction, the Auto-Regressive Integrated Moving Average (ARIMA) algorithm is used. You can mix the blend of algorithms used by using the mining model parameters.
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Listing 11.8 A value converter that converts a Boolean to Yes or No (C#)
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Figure 1.6 Version 1.4 of our application anchors the picture box control to all sides of the window, so that it resizes automatically whenever the window is resized.
The context and initParams serve as valuable initializers in the plug-in creation process, which involves deciding how to handle the onError and onLoad events impacting the plug-in. The initial look of this plug-in is set up through a variety of property settings declared within the properties sub-object. Initialization parameters and context are great ways to get simple values to Silverlight applications running in the browser. But, keep in mind that those values don t currently carry over to applications running out of the browser something we ll cover in chapter 5.
ADDING A MONTHCALENDAR CONTROL An overview of the MonthCalendar class is provided in .NET Table 11.5. This class handles the entire range of dates possible in DateTime objects, which is basically any date with a four-digit century. This class is a good way to display a series of dates related to an object or collection of objects.
You are probably going to be enrolling as an individual, so click the Individual link as illustrated in Figure 1 4. If you are enrolling as a company, click the Company option to the right and follow the appropriate steps; I ll meet you at step 6.
As you can see, the text is truncated on the label and on the buttons. The default behavior of controls is to fit their content, provided you don t force a size. We could remove the Width from the label and the buttons (figure 5.20).
Having read this book so far, you should be familiar with what a business entity looks like, what a domain model is, and roughly how a domain model is formed. Our examples have aimed to keep things simple, so we haven t yet introduced you to the processes and techniques that will help you tackle real-world projects. The first part of this chapter looks at the various starting points of an NHibernate project and then explains how you can leverage automation and generation to help build the other layers. Until now, you ve been manually implementing entities by hand. You can save much time by using the tools described here to automatically generate domain-model entities, database schema, and even mapping definitions.
matching process 162 matching quote 39 [math], sqrt() 384 MD5 hash algorithm 441 Measure-Object cmdlet 490 megabytes 65 member collection 235 setting the property 236 member types 222, 224 members 158 memory consumption 256 resource management 330 Menu 379 menu, displaying 363 MenuBar, Internet Explorer 400 MenuItem 379 merging streams in assignment statements 140 message box 403 metacharacters 312 metacharacters in files 309 metaprogramming 214, 249 method argument commas 299 method call 135, 283 C# vs PowerShell 495 syntax 135 method invocations 135 methods 136, 225 Microsoft Developer Network 234, 348 Microsoft Exchange 293 Microsoft Management Console (mmc.exe) 471 Microsoft Office applications 405 Manipulating with COM 409 Microsoft security response 442 MinusMinusToken, PowerShell grammar 529 missing hot fixes 503 mitigation, definition 445 Listing 7.6 shows the complete code for class PubControlModuleImpl, which implements our new module s interface. Most of the class is boilerplate that is, code you have to write to implement any module. The important part is the draft property, which is a boolean b, and its getter C and setter D methods.
Isolation (mock object) frameworks
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