Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Survival Tree

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...

Introduction to R

Introduction to R

R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's...

Contingency Table

Contingency Table

A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...

Interpreting R Charts

Interpreting R Charts

R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...

Decision Making: P-value Method

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can...

Regression Analysis

Regression Analysis

Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:

You might also read

Related Articles

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

Sort by

Same author

HRRT: hierarchical reinforcement learning for renal replacement therapy decision support.

NPJ digital medicine·2026

Same author

Large language models in emergency and critical care medicine: a comprehensive review of applications, challenges, and future directions.

Burns & trauma·2026

Same author

Systematic evaluation of the DeepSeek large language model for clinical diagnostic reasoning.

PloS one·2026

Same author

BRD4 modulates antimicrobial defense via non-canonical NRF2 activation in macrophages to confer protection against sepsis.

PLoS pathogens·2026

Same author

CT-based AI system for quantitative and integrated management of acute respiratory distress syndrome in critical care.

NPJ digital medicine·2026

Same author

Multicenter validation and updating of the ELDER-ICU model for severity assessment in elderly critical illness.

NPJ digital medicine·2026

Same journal

Towards haplotypes of blood group genes: the impact of long-read sequencing in molecular immunohematology.

Annals of translational medicine·2026

Same journal

Development of pharmacological interventions for the treatment of sarcopenia.

Annals of translational medicine·2026

Same journal

Fertility preservation in young women with breast cancer: a narrative review of effectiveness, oncologic safety, and clinical practice implications.

Annals of translational medicine·2026

Same journal

Propofol-based total intravenous anesthesia and recurrence-free survival after hepatectomy-does it improve outcomes?

Annals of translational medicine·2026

Same journal

Is pulmonary hypertension still a contraindication for lung volume reduction?-a narrative review of contemporary evidence.

Annals of translational medicine·2026

Same journal

Calcium montmorillonite clay: a clinically oriented narrative review of emerging perioperative and supportive applications.

Annals of translational medicine·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 15, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Decision tree modeling using R.

Zhongheng Zhang¹

¹Department of Critical Care Medicine, Jinhua Municipal Central Hospital, Jinhua Hospital of Zhejiang University, Jinhua 321000, China.

Annals of Translational Medicine

|August 30, 2016

Summary

This summary is machine-generated.

Decision trees offer interpretable machine learning via recursive partitioning. Random forests enhance stability by using random sampling and variable selection, while model-based partitioning integrates these methods into parametric modeling.

Keywords:

Machine learning R conditional inference decision trees random forests recursive partitioning

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Related Experiment Videos

Last Updated: Mar 15, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Area of Science:

Machine Learning
Statistical Modeling

Background:

Decision tree learners are powerful and interpretable machine learning tools.
They utilize recursive binary partitioning to split data based on variable association with the response.
Single decision trees can be sensitive to minor data changes.

Purpose of the Study:

To explore conditional inference trees and random forests for improved decision tree modeling.
To introduce R functions for model-based recursive partitioning.
To integrate recursive partitioning with conventional parametric model building.

Main Methods:

Focus on conditional inference trees, integrating tree-structured regression with conditional inference.
Implementation of the random forests procedure using random sampling and restricted input variable selection.
Development of R functions for model-based recursive partitioning.

Main Results:

Conditional inference trees provide a structured approach to regression modeling.
Random forests mitigate the instability of single decision trees.
Model-based recursive partitioning offers a hybrid approach to statistical modeling.

Conclusions:

Conditional inference trees and random forests are valuable extensions of decision tree methodology.
Model-based recursive partitioning provides a flexible framework for integrating partitioning with parametric models.
The R functions facilitate the application of these advanced techniques.