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Related Concept Videos

Survival Tree01:19

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 survival tree begins...
Compacting Factor test01:22

Compacting Factor test

The compacting factor test is a method used to assess the workability of concrete. It is  especially suitable for concrete mixes containing aggregates up to one and a half inches in size. This test involves specialized equipment consisting of two truncated cone-shaped hoppers and a cylinder, all with polished interior surfaces to minimize friction.
The procedure begins by placing concrete into the upper hopper without any compaction. Once filled, the bottom door of this hopper is opened,...
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an organic...
Trimmed Mean01:10

Trimmed Mean

While measuring the mean of a data set, care needs to be taken when associating the mean to its central tendency. The same goes for the arithmetic mean, the geometric mean, or the harmonic mean. This is because the presence of a single outlier data value can significantly affect the mean. That is, the mean is sensitive to fluctuations in the data set.
Although certain measures of central tendency are not sensitive to outliers, there are alternative versions of the mean that get around the...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

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Related Experiment Video

Updated: Jun 9, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Fast Calculation of Feature Contributions in Boosting Trees.

Zhongli Jiang1, Min Zhang1, Dabao Zhang1

  • 1Department of Epidemiology & Biostatistics, University of California, Irvine, CA, USA.

Proceedings of Machine Learning Research
|June 8, 2026
PubMed
Summary
This summary is machine-generated.

We introduce Q-SHAP, an efficient algorithm for calculating Shapley values with quadratic losses. This method improves computational speed and accuracy for feature contribution analysis in tree models.

Related Experiment Videos

Last Updated: Jun 9, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

  • Machine Learning
  • Explainable AI
  • Computational Statistics

Background:

  • Fast algorithms exist for Shapley value decomposition in tree models, enabling local feature attribution.
  • Global evaluation of feature contributions is needed, but individualizing coefficients of determination (R^2) is difficult due to quadratic losses.

Purpose of the Study:

  • To propose Q-SHAP, an efficient algorithm for calculating Shapley values under quadratic losses.
  • To improve computational efficiency and accuracy in feature-specific R^2 estimation.

Main Methods:

  • Developed Q-SHAP, an algorithm reducing Shapley value computation for quadratic losses to polynomial time.
  • Conducted simulations to evaluate Q-SHAP's performance.

Main Results:

  • Q-SHAP significantly improves computational efficiency.
  • Q-SHAP enhances the accuracy of feature-specific R^2 estimates.

Conclusions:

  • Q-SHAP provides an efficient and accurate method for global feature contribution analysis in tree models.
  • The algorithm addresses the challenges of individualizing R^2 with quadratic losses.