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    <p>A decision tree tells you in which order to look at the features of an item to classify that item.</p> <p>Obviously, you can look at the features in any order you like until you have reached a decision about how to classify an item. This could potentially result in a large and therefore very specific decision tree. This is called <em>overfitting</em>. A small decision tree is preferred, because a small decision tree is less likely to classify the examples correctly and new items incorrectly.</p> <p>ID3 is an algorithm for creating <em>small</em> decision trees, that classifies the examples correctly. It does that by calculating the entropy of the features. The entropy tells you, how much information you gain by examining that feature. So the first question you have to answer is: <strong>what are the features of the items you want to classify?</strong></p> <p>For example to classify project offers into <em>accept</em> and <em>reject</em>, you might want to look at past projects and record the following features for each:</p> <ul> <li>How long did it take to complete?</li> <li>How much did you get paid (effectively per invested hour)?</li> <li>Which tools and languages where used?</li> <li>How interested are you in the software personally?</li> <li>Who challenging was the project?</li> <li>Was the work done at home or on site?</li> <li>Was the communication with the customer satisfactory?</li> </ul> <p>Also record whether you would do the same project again. Use this to classify the projects. Now you can build a decision tree based on the features of your past projects and their classification.</p>
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    1. POhow can I create decision tree (ID3)?
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    1. COI don't understand what you are talking about. I must classify document5 to class1 or class2 and I want to use decision tree algorithm. So what should I compute now?
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    2. COYou cannot use a decision tree unless you have worked out what the *features* of your documents are and how high each example scores at a particular feature. I cannot help you with this step, because each problem is unique in this respect. Choose irrelevant features and you get a poor decision tree; regardless of which algorithm you use for constructing the decision tree.
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    3. COSo could you tell me what other algorithm will be good for me for classify documents (but I want other algorithm than naive bayes or knn) ??
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