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2 results

  • Extended Similarity Trees

  • James E. Corter, Amos Tversky
  • Journal:
    Psychometrika / Volume 51 / Issue 3 / September 1986
    Published online by Cambridge University Press:
    01 January 2025, pp. 429-451

  • Proximity data can be represented by an extended tree, which generalizes traditional trees by including marked segments that correspond to overlapping clusters. An extended tree is a graphical representation of the distinctive features model. A computer program (EXTREE) that constructs extended trees is described and applied to several sets of conceptual and perceptual proximity data.

  • 8 - Tree-Based Classification and Regression

  • John H. Maindonald, Statistics Research Associates, Wellington, New Zealand, W. John Braun, University of British Columbia, Okanagan, Jeffrey L. Andrews, University of British Columbia, Okanagan
  • Book:
    A Practical Guide to Data Analysis Using R
    Published online:
    11 May 2024
    Print publication:
    30 May 2024, pp 373-399

  • Summary

    Tree-based methods use methodologies that are radically different from those discussed in previous chapters. They are relatively easy to use and can be applied to a wide class of problems. As with many of the new machine learning methods, construction of a tree, or (in the random forest approach, trees) follows an algorithmic process. Single-tree methods occupy the first part this chapter. An important aspect of the methodology is the determining of error estimates. By building a large number of trees and using a voting process to make predictions, the random forests methodology that occupies the latter part of this chapter can often greatly improve on what can be achieved with a single tree. The methodology operates more as a black box, but with implementation details that are simpler to describe than for single- tree methods. In large sample classification problems, the methodology has often proved superior to other contenders.