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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. Card sorting - Wikipedia

   en.wikipedia.org/wiki/Card_sorting

   Card sorting is a technique in user experience design in which a person tests a group of subject experts or users to generate a dendrogram (category tree) or folksonomy. It is a useful approach for designing information architecture, workflows, menu structure, or web site navigation paths. Card sorting uses a relatively low-tech approach.

4. 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 ...

5. Usability testing - Wikipedia

   en.wikipedia.org/wiki/Usability_testing

   In web development and marketing, A/B testing or split testing is an experimental approach to web design (especially user experience design), which aims to identify changes to web pages that increase or maximize an outcome of interest (e.g., click-through rate for a banner advertisement).

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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8. Bootstrap aggregating - Wikipedia

   en.wikipedia.org/wiki/Bootstrap_aggregating

   As most tree based algorithms use linear splits, using an ensemble of a set of trees works better than using a single tree on data that has nonlinear properties (i.e. most real world distributions). Working well with non-linear data is a huge advantage because other data mining techniques such as single decision trees do not handle this as well.

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