C4.5 learner by Ross Quinlan
Classifier
, C45 Tree
and Classification Tree
are available only if examples are present on the input. Which of the latter two output signals is active is determined by setting Convert to orange tree structure (see the description below.
This widget provides a graphical interface to the well-known Quinlan's C4.5 algorithm for construction of classification tree. Orange uses the original Quinlan's code which must be, due to copyright issues, built and linked in separately.
Orange also implements its own classification tree induction algorithm which is comparable to Quinlan's, though the results may differ due to technical details. It is accessible in widget Classification Tree
.
As all widgets for classification, C4.5 widget provides learner and classifier on the output. Learner is a learning algorithm with settings as specified by the user. It can be fed into widgets for testing learners, namely Test Learners
. Classifier is a classification tree build from the training examples on the input. If examples are not given, the widget outputs no classifier.
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Learner can be given a name under which it will appear in, say, The next block of options deals with splitting. C4.5 uses gain ratio by default; to override this, check Use information gain instead of ratio, which is equivalent to C4.5's command line option As for pruning, you can set the Minimal number of examples in the leaves (Quinlan's default is 2, but you may want to disable this for noiseless data), and the Post prunning with confidence level; the default confidence is 25. Trees can be constructed iteratively, with ever larger number of examples. If enable, you can set the Number of trials, the initial windows size and window increment. The resulting classifier can be left in the original Quinlan's structure, as returned by his underlying code, or converted to orange the structure that is used by Orange's tree induction algorithm. This setting decides which of the two signals that output the tree - When you change one or more settings, you need to push Apply Setting; this will put the new learner on the output and, if the training examples are given, construct a new classifier and output it as well. |
There are two typical uses of this widget. First, you may want to induce the tree and see what it looks like, like in the schema on the right.
The second schema shows how to compare the results of C4.5 learner with another classifier, naive Bayesian Learner.