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相关概念视频

Bias01:22

Bias

4.1K
Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
4.1K
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

87
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
87
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

190
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
190
Multiple Regression01:25

Multiple Regression

3.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.0K
Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Regression Analysis01:11

Regression Analysis

5.7K
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:
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相关实验视频

Updated: Jun 17, 2025

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

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解决包装偏差问题,并加强回归模型.

Juliette Ugirumurera1, Erik A Bensen2, Joseph Severino3

  • 1Computational Science Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO, 80401, USA. jugirumu@nrel.gov.

Scientific reports
|August 8, 2024
PubMed
概括

本研究引入了一种新方法来减少机器学习 (ML) 回归模型中的偏差,显著提高了人工智能应用中少数群体的公平性. 这种方法有效地减轻了树基模型中的偏差超过50%.

关键词:
人工智能的人工智能是人工智能.机器学习中的偏差公平的机器学习渐变增强树木的树木.随机的森林随机的森林在XGBoost上使用.

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An R-Based Landscape Validation of a Competing Risk Model
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An R-Based Landscape Validation of a Competing Risk Model

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

Published on: July 3, 2020

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相关实验视频

Last Updated: Jun 17, 2025

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

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An R-Based Landscape Validation of a Competing Risk Model
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An R-Based Landscape Validation of a Competing Risk Model

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

  • 机器学习 机器学习
  • 人工智能的人工智能
  • 数据科学数据科学数据科学

背景情况:

  • 越来越多的人工智能使用需要在ML模型中进行偏差调查.
  • 之前的研究集中在分类上,忽视了回归模型偏差.
  • 对于许多应用来说至关重要的回归模型经常表现出性能差异.

研究的目的:

  • 提出一种新的,可访问的方法来缓解回归模型中的偏差.
  • 将偏差缓解技术扩展到包装和增强组合方法.
  • 严格测量和减少与受保护属性相关的偏见.

主要方法:

  • 开发了一种偏差缓解技术,适用于最小化可微分损失函数的模型.
  • 在模型的损失函数中集成了一个规范化术语,以惩罚与受保护属性的错误相关性.
  • 在随机森林,梯度增强树木和XGBoost模型上应用和验证了该方法.

主要成果:

  • 提出的方法有效地减少了基于树的集合回归模型中的偏差.
  • 预测道路交通量模型中的偏差在少数民族居住地区减少了50%以上.
  • 尽管总体准确度很高,但基线模型在少数民族地区的道路上表现不佳.

结论:

  • 提出的方法提供了一种有效的解决方案,以减轻回归ML模型中的偏差.
  • 这种方法在不同的人口群体中提高了公平和公平的表现.
  • 该技术广泛适用于通过可差分损失最小化训练的各种ML模型.