Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Combining classification trees using MLE.

W D Shannon1, D Banks

  • 1Washington University School of Medicine, Division of General Medical Sciences, St. Louis, MO 63110, USA. shannon@osler.wustl.edu

Statistics in Medicine
|April 16, 1999
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Equal to the challenge.

Nursing standard (Royal College of Nursing (Great Britain) : 1987)·2016
Same author

Mental health nursing and the problematic of supervision as a confessional act.

Journal of psychiatric and mental health nursing·2012
Same author

Co-producing social inclusion: the structure/agency conundrum.

Journal of psychiatric and mental health nursing·2012
Same author

Randomized controlled trial assessing participation and quality of life in a supported speed treadmill training exercise program vs. a strengthening program for children with cerebral palsy.

Journal of pediatric rehabilitation medicine·2012
Same author

[Sulfur metabolite bacteria from waste water of gold miner tale-depot in Kuzbass].

Mikrobiologiia·2009
Same author

Pharmacogenetic analysis of paclitaxel transport and metabolism genes in breast cancer.

The pharmacogenomics journal·2007
Same journal

Interpretable Bayesian Modeling for Multireader Multicase Studies: Addressing Overdispersion and Limited Sample Size in Diagnostic Enhancement Evaluation.

Statistics in medicine·2026
Same journal

Adaptive Sequential Multiple Hypotheses Testing for Concomitant Vaccine Safety Surveillance.

Statistics in medicine·2026
Same journal

Novel Distance Regression for Repeated Outcomes With Missing Data: Applications to Longitudinal and Crossover Studies of Microbiome Beta-Diversity.

Statistics in medicine·2026
Same journal

Optimal Weighted Tests for Replication Studies and the 'Two-Trials Rule' With Multiple Hypotheses.

Statistics in medicine·2026
Same journal

Identifiable Copula-Double-Cox Models: A Fully Parametric Framework for Dependent Right-Censored Survival Data.

Statistics in medicine·2026
Same journal

Moving From Individualized Risk-Based Prevention to Benefit-Based Prevention: Estimating Individualized Life-Years Gained From Prevention Services as a Basis for Eligibility.

Statistics in medicine·2026
See all related articles

We developed a new probability distribution for classification trees, focusing on structure over cutpoints. The maximum likelihood estimate provides an interpretable and generalizable central tree model from variable tree data.

Area of Science:

  • Machine Learning
  • Statistical Modeling
  • Bioinformatics

Background:

  • Classification trees are widely used for predictive modeling.
  • Variability in tree structures can arise from different data subsets or algorithms.
  • Existing methods may not effectively capture the central tendency of a tree ensemble.

Purpose of the Study:

  • To propose a novel probability distribution for equivalence classes of classification trees.
  • To model observed variability in classification tree structures.
  • To identify a representative and generalizable tree structure from a set of trees.

Main Methods:

  • Developed a probability distribution for classification trees based on structure, not cutpoint values.
  • Parameterization includes a central tree structure and a concentration coefficient.

Related Experiment Videos

  • Employed maximum likelihood estimation (MLE) to find the central tree.
  • Utilized an ascent search algorithm for MLE tree structure identification.
  • Applied the method to a dataset of 13 classification trees predicting cancer presence.

    • Main Results:

    • The proposed distribution effectively models ensembles of classification trees with structural variability.
    • The maximum likelihood estimate (MLE) of the central tree provides an interpretable and generalizable model.
    • The MLE approach successfully identified a representative tree structure from the dataset.

    Conclusions:

    • The developed probability distribution offers a robust framework for analyzing sets of classification trees.
    • The MLE central tree is a powerful tool for summarizing complex tree ensembles.
    • This approach enhances interpretability and generalizability in tree-based predictive modeling, particularly in fields like cancer prediction.