J The C algorithm for building decision trees is implemented in Weka as a classifier called J Classifiers, like filters, are organized in a. is carried out, using the modified J48 decision tree algorithm. The implementation of the decision tree algorithm and the identified results are discussed in this. A decision tree learning algorithm will return a predictive model which could be of the visual version of the decision tree for the J48 algorithm.


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J48 decision tree - Mining at UOC

The other attributes, which help in predicting the value j48 classifier the dependent variable, are known as the independent variables in the dataset. The J48 Decision tree classifier follows the following simple algorithm.


In order to classify a new item, it first needs to create a decision tree based on the attribute j48 classifier of the available training data. So, whenever it encounters a j48 classifier of items training set it identifies the attribute that discriminates the various instances most clearly.

Decision trees explained using Weka | technobium

This feature that is able to tell us most about the data instances so that we can classify them the best is said to have the highest information gain. Now, among the possible values of this feature, if there is any value for which there is no ambiguity, that is, for which the data instances falling within its category have the same value for the target variable, then we terminate that branch and assign to it the target value that we have obtained.

For the other cases, we then look for another attribute that gives us the highest information gain. Hence we continue in this manner until we either get a clear decision of what combination of attributes gives us a particular target value, or we run out of attributes.

j48 classifier


In the event that we run out of attributes, or if we cannot get an unambiguous result from the available information, we assign this branch a target value that the majority of the items under this branch possess.

As you can see, each level splits the data j48 classifier to different attributes, the non-leaf nodes are represented by attributes, the leaf nodes represent the predicted variable — in our case Success or Failure.

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The j48 classifier algorithm for learning decision trees is: The answer for this is the information gain concept. Information gain is the difference between the entropy before and after a decision. In our example, we must select the attribute which lowers the entropy.

The formula for the j48 classifier is the following: Confusion Matrix is telling the following: The decision tree has classified 50 Setosa objects as Setosa.

The decision tree has classified 49 Versicolor objects as Versicolor and 2 as Virginica, leading in 2 misclassifications. The decision tree has classified 48 Virginica objects as Virginica and 1 as Versicolor, leading in 1 misclassification.

Open questions for students The decision tree has taken value 0.

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