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All of this leads to a de nition: The best split for an attribute is the one that gives the maximum information gain ratio, de ned as Information-gain(S, {S1 , S2 , , Sr }) Information-content(S, {S1 , S2 , , Sr }) Finding Best Splits How do we nd the best split for an attribute How to split an attribute depends on the type of the attribute Attributes can be either continuous valued, that is, the values can be ordered in a fashion meaningful to classi cation, such as age or income, or can be categorical, that is, they have no meaningful order, such as department names or country names We do not expect the sort order of department names or country names to have any signi cance to classi cation Usually attributes that are numbers (integers/reals) are treated as continuous valued while character string attributes are treated as categorical, but this may be controlled by the user of the system In our example, we have treated the attribute degree as categorical, and the attribute income as continuous valued We rst consider how to nd best splits for continuous-valued attributes For simplicity, we shall only consider binary splits of continuous-valued attributes, that is, splits that result in two children The case of multiway splits is more complicated; see the bibliographical notes for references on the subject To nd the best binary split of a continuous-valued attribute, we rst sort the attribute values in the training instances We then compute the information gain obtained by splitting at each value For example, if the training instances have values 1, 10, 15, and 25 for an attribute, the split points considered are 1, 10, and 15; in each case values less than or equal to the split point form one partition and the rest of the values form the other partition The best binary split for the attribute is the split that gives the maximum information gain For a categorical attribute, we can have a multiway split, with a child for each value of the attribute This works ne for categorical attributes with only a few distinct values, such as degree or gender However, if the attribute has many distinct values, such as department names in a large company, creating a child for each value is not a good idea In such cases, we would try to combine multiple values into each child, to create a smaller number of children See the bibliographical notes for references on how to do so Decision-Tree Construction Algorithm The main idea of decision tree construction is to evaluate different attributes and different partitioning conditions, and pick the attribute and partitioning condition that results in the maximum information gain ratio The same procedure works recur-.



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HI all I have a urgent requirement i want to convert . tiff image to . pdf file Thanks in advance. ... Try to use PDF Vision (for c#, asp.net, vb . net ).

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HI all I have a urgent requirement i want to convert . tiff image to . pdf file Thanks in advance. ... Try to use PDF Vision (for c#, asp.net, vb . net ).

the original square, but don t release the mouse button yet There are two spaces sidling this line that represent half an inch

The McGraw Hill Companies, 2001

buttons What you ve done is duplicate the original square to a new position This is an important, quick technique for duplicating shapes

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6 Repeat steps 4 and 5 to build the array of evenly spaced squares, as shown in Figure 6-9





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procedure GrowTree(S) Partition(S); procedure Partition (S) if (purity(S) > p or |S| < s ) then return; for each attribute A evaluate splits on attribute A; Use best split found (across all attributes) to partition S into S1 , S2 , , Sr ; for i = 1, 2, , r Partition(Si ); Figure 227 Recursive construction of a decision tree

Notice that when an object s edge is on, or even close to, the Grid when Snap To is turned on, you ll see a tiny blue label indicating the snap to point You re not limited to shape edges as the origin of a snapping point; setting behavior for snapping origins is covered later in this chapter Snapping and snapping behavior (discussed in the following section) aren t limited to the Grid In fact, you don t even have to have the Grid turned on to snap objects to one another; you press ALT+Z and then all your objects are sticky

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FIGURE 6-9

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Collections.Generic; using System.Text; using PdfSharp.Drawing; using PdfSharp. Pdf ; namespace ConsoleApplication1 { class Program { static ...

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