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相关概念视频

Chebyshev's Theorem to Interpret Standard Deviation01:15

Chebyshev's Theorem to Interpret Standard Deviation

4.2K
Chebyshev’s theorem, also known as Chebyshev’s Inequality, states that the proportion of values of a dataset for K standard deviation is calculated using the equation:
4.2K
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

2.2K
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...
2.2K
Kendall's Coefficient of Concordance01:20

Kendall's Coefficient of Concordance

278
Kendall's Coefficient of Concordance (W), also known as Kendall's W, is a non-parametric statistical measure used to assess the agreement or concordance between multiple raters or judges when they rank a set of items. It is often used when you have ordinal data (ranks) and you want to see if there is consistency or consensus among the raters. It is widely applied in research areas such as psychology, medicine, and social sciences, where multiple judges are asked to rank or rate subjects...
278
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

421
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
421
Wilcoxon Signed-Ranks Test for Matched Pairs01:09

Wilcoxon Signed-Ranks Test for Matched Pairs

94
The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
94
Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

5.9K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
5.9K

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相关实验视频

Updated: Jun 12, 2025

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

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使用二进制特征计算和比较支持向量机模型中不同内核的确切Shapley值的协议.

Jannik P Roth1, Jürgen Bajorath1

  • 1Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, 53115 Bonn, Germany; Lamarr Institute for Machine Learning and Artificial Intelligence, Friedrich-Hirzebruch-Allee 5/6, 53115 Bonn, Germany.

STAR protocols
|November 14, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了在机器学习模型中计算精确Shapley值的协议,特别是支持矢量机器. 这种方法提高了AI预测的可解释性,使用游戏理论概念.

关键词:
生物信息学是一种生物信息学.化学 化学 化学 化学计算机科学 计算机科学

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Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification
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Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

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相关实验视频

Last Updated: Jun 12, 2025

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

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification
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Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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科学领域:

  • 计算化学是一种计算化学.
  • 机器学习是机器学习.
  • 人工智能的人工智能是人工智能.

背景情况:

  • 机器学习模型,特别是支持向量机 (SVM),越来越多地用于科学研究.
  • 解释这些复杂模型的预测对于信任和验证至关重要.
  • 沙普利的价值形式主义提供了一个理论上合理的方法,用于归因对模型预测的贡献.

研究的目的:

  • 为 SVM 模型提供一个详细的协议来计算精确的 Shapley 值.
  • 为了能够在不同的内核和二进制输入特征中比较Shapley值.
  • 为分析和解释SVM预测中的特征重要性提供一个框架.

主要方法:

  • 从合作游戏理论中调整了沙普利的价值形式主义.
  • 开发一个用于计算SVM的精确Shapley值的协议.
  • 使用可定制的Python脚本用于数据准备和值计算.
  • 实施相关性分析和特征映射以解释结果.

主要成果:

  • 建立了一个可复制的协议,用于计算SVM模型的确切Shapley值.
  • 该协议有助于在各种SVM配置中比较功能重要性.
  • 详细介绍了分析和可视化Shapley值结果的方法.

结论:

  • 本协议提供了一种强大的方法来提高SVM模型的可解释性.
  • 这种方法可以更深入地了解特征对模型预测的贡献.
  • 沙普利的价值形式主义,当应用到SVM时,为AI模型行为提供了有价值的见解.