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Related Concept Videos

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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.
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Interpretation of Confidence Intervals

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  3. Expected-depth Selection For Interpretable Decision Trees.
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  3. Expected-depth Selection For Interpretable Decision Trees.

Related Experiment Video

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

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

Published on: January 11, 2020

Expected-depth selection for interpretable decision trees.

Zhigao Huang1, Yuzhuo Pan1, Miao Pan2

  • 1Key Laboratory of Information Functional Material for Fujian Higher Education, Quanzhou Normal University, Quanzhou, 362000, China.

Scientific Reports
|May 27, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces soft-depth (Soft-D) selection for decision trees, optimizing for average inference cost (expected depth) instead of worst-case measures. Soft-D significantly reduces expected depth while maintaining accuracy, improving model efficiency.

Keywords:
Average-case complexityCost-sensitive inferenceDecision treesExplainable data miningInterpretabilityModel selectionPruning

Related Experiment Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

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

Published on: January 11, 2020

Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Data Science

Background:

  • Traditional decision tree selection relies on worst-case metrics like maximum depth.
  • Model deployment often incurs costs based on the specific root-to-leaf path used for inference.
  • Expected depth, representing the average inference burden, is a more relevant metric for many applications.

Purpose of the Study:

  • To introduce and evaluate a novel method for selecting decision trees based on expected depth.
  • To compare the proposed soft-depth (Soft-D) selector against existing methods like CART and accuracy-first cost-complexity pruning (CCP).
  • To analyze the impact of expected depth selection on model accuracy and complexity.

Main Methods:

  • The soft-depth (Soft-D) selector identifies trees with minimum validation expected depth within an accuracy tolerance of the best candidate.
  • Evaluation across eleven benchmark datasets with 25 outer test folds per dataset.
  • Comparison with unpruned CART, accuracy-first CCP, and a new depth-limited CART baseline.

    • Main Results:

    • Soft-D reduced expected depth by 47.4% compared to unpruned CART and 12.1% compared to accuracy-first CCP.
    • The gains were most significant when a practitioner-selected maximum depth still allowed for long paths.
    • Analysis confirmed expected depth selection as a viable deployment-time strategy, distinct from global tree induction optimization.

    Conclusions:

    • Expected depth selection offers a lightweight, deployment-time approach to optimize decision tree inference costs.
    • Soft-D provides a practical alternative to worst-case structural measures for interpretable decision trees.
    • The method effectively balances accuracy and average inference burden, enhancing practical model deployment.