In the application that I\'m working on right now, I need to periodically check eligibility of tens of thousands of objects for some kind of a service. The decision diagram itself is in the following
if i got two decision trees on th开发者_JAVA技巧e same amount of nodes, which is considered better?
after studying decision tree for a while, I noticed there is a small technique called boosting. I see in normal cases, it will improve the accuracy of the decision tree.
I have just spent a couple of hours trying to represent the decision tree for the quick开发者_StackOverflowsort algorithm on a set of elements (and I also searched the web). I would like to know what
I want to know if I build up a decision tree A like ID3 from training and validation set,but A is unpruned.
I a开发者_如何转开发m writing my own code for a decision tree. I need to decide on when to terminate the tree building process. I could think of limiting the height of the tree, but this seems trivial
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In ID3 implementation, at w开发者_StackOverflow中文版hich point the recursion in Algorithm should stop.A branch stops when there are no examples left to classify or there are no attributes left to cla
I\'m looking for a java lib开发者_运维技巧rary for decision trees which accepts numeric attributes and classes/grades. Weka\'s J48 deals with discrete attributes but doesn\'t accept numeric ones.
If 开发者_如何学JAVAI understand it right, both use Bayes Theorem to generate an acyclic graph and calculate percentages based on functions applied at every node.
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