Sentence examples for decision tree learner from inspiring English sources

J48 is Weka's implementation of a C4.5 decision tree learner.

We compare SORCER with IDTM, a system that induces standard decision tables, and a state-of-the-art decision tree learner, C4.5.

For the cost-sensitive classification, Weka's implementations of the Support Vector Machine and C4.5 decision tree learner have performed relatively well.

We compare two different rule-induction techniques, both popular in language engineering applications, viz. the rule sequence learner Transformation-Based Error-Driven Learning (TBrill Brill (1995) and the decision tree learner C5.0 Quinlan (1993).

Third, our experiments showed that for the decision tree learner C5 alignment-based features are very useful, but that a combination with position-specific features and certain chemical features is necessary for obtaining the best results.

Furthermore, as the P-gp model was built using a decision tree learner it is worth monitoring misprediction occurrence with respect to chemical span in the decision tree's branches.

MyC, a locally developed decision-tree learner.

C4.5 [ 50], a well-known decision-tree learner.

Since each decision tree base learner implies a hierarchy of informative features, we can average over the boosting rounds to produce an aggregate view of the most important features in the classification task.

Some models induced by machine learning approaches, however, are relatively easy to understand, like decision trees, rule learners or nearest neighbor models.

In this paper a rule based approach with a family of six decision tree classifiers viz., Alternating Decision Tree, Decision Stump, J48, Logical Model Tree, Naïve Baye's Tree and Fast Decision Tree learner, to perform the detection of audio subliminal channel is introduced.