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  2. Tree testing - Wikipedia

    en.wikipedia.org/wiki/Tree_testing

    Tree testing is a usability technique for evaluating the findability of topics in a website. [1] It is also known as reverse card sorting or card-based classification. [2] A large website is typically organized into a hierarchy (a "tree") of topics and subtopics. [3] [4] Tree testing provides a way to measure how well users can find items in ...

  3. Classification Tree Method - Wikipedia

    en.wikipedia.org/wiki/Classification_Tree_Method

    The Classification Tree Method is a method for test design, [1] as it is used in different areas of software development. [2] It was developed by Grimm and Grochtmann in 1993. [3] Classification Trees in terms of the Classification Tree Method must not be confused with decision trees. The classification tree method consists of two major steps ...

  4. Tree network - Wikipedia

    en.wikipedia.org/wiki/Tree_network

    A tree topology, or star-bus topology, is a hybrid network topology in which star networks are interconnected via bus networks. [ 1 ] [ 2 ] Tree networks are hierarchical, and each node can have an arbitrary number of child nodes.

  5. Chi-square automatic interaction detection - Wikipedia

    en.wikipedia.org/wiki/Chi-square_automatic...

    Like other decision trees, CHAID's advantages are that its output is highly visual and easy to interpret. Because it uses multiway splits by default, it needs rather large sample sizes to work effectively, since with small sample sizes the respondent groups can quickly become too small for reliable analysis.

  6. Tree test - Wikipedia

    en.wikipedia.org/wiki/Tree_test

    Tree test may mean: Tree testing, a method of evaluating topic trees for findability; Baum test, projective drawing technique developed by Karl Koch

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  9. Training, validation, and test data sets - Wikipedia

    en.wikipedia.org/wiki/Training,_validation,_and...

    A test data set is a data set that is independent of the training data set, but that follows the same probability distribution as the training data set. If a model fit to the training data set also fits the test data set well, minimal overfitting has taken place (see figure below). A better fitting of the training data set as opposed to the ...