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Sep 27, 2011 · Attribute selection is the fundamental step to construct a decision tree. There two term Entropy and Information Gain is used to process attribute selection, using attribute selection ID3 algorithm select which attribute will be selected to become a node of the decision tree and so on.

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Apr 23, 2017 · Now that we know what entropy is, let’s look at an attribute that is more attached to the building of the decision tree - Information Gain. Information gain is a metric that measures the expected reduction in the impurity of the collection S, caused by splitting the data according to any given attribute.

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The unit of entropy Shannon chooses, is based on the uncertainty of a fair coin flip, and he calls this "the bit", which is equivalent to a fair bounce. We can arrive at the same result using our bounce analogy. Entropy or H is the summation for each symbol, of the probability of that symbol times the number of bounces.

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'Decision tree learning is a method for approximating discrete-valued target functions, in which the learned function is represented by a decision tree. Decision tree learning is one of the most widely used and practical methods for inductive inference'. (Tom M. Mitchell,1997,p52) Decision tree learning algorithm has been successfully used in ...

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In general, exploring your data with a decision tree is a good idea, applying the model on unseen data not always. You may preprocess your data by applying the operator "Sample (Bootstrapping)" but you should switch off preprocessing in the testing step. For further documentation please refer to the documentation of the decision tree operator.

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May 20, 2017 · This attribute minimizes the information needed to classify the samples in the resulting partitions. Entropy, in general, measures the amount of disorder or uncertainty in a system. In the classification setting, higher entropy (i.e., more disorder) corresponds to a sample that has a mixed collection of labels.