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相关概念视频

Stereotype Content Model02:16

Stereotype Content Model

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Regression Analysis01:11

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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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Response Surface Methodology01:16

Response Surface Methodology

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Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
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Stereotypes, Prejudice, and Discrimination02:55

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Humans are very diverse and although we share many similarities, we also have many differences. The social groups we belong to help form our identities (Tajfel, 1974). These differences may be difficult for some people to reconcile, which may lead to prejudice toward people who are different. Prejudice is a negative attitude and feeling toward an individual based solely on one’s membership in a particular social group (Allport, 1954; Brown, 2010). Prejudice is common against people who...
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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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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...
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相关实验视频

Updated: May 24, 2025

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一个优化的机器学习框架,用于预测和解释企业的ESG绿色洗行为.

Fanlong Zeng1, Jintao Wang2,3, Chaoyan Zeng3

  • 1School of Foreign Studies, Yiwu Industrial and Commercial College, Jinhua, Zhejiang, China.

PloS one
|March 6, 2025
PubMed
概括

本研究介绍了一种优化的机器学习模型,用于预测企业的环境,社会和治理 (ESG) 绿色洗. IHPO-XGBoost框架显著提高了预测准确性和可解释性,有助于监管监督.

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科学领域:

  • 环境,社会和治理 (ESG) 分析
  • 机器学习在金融中的应用.
  • 公司的可持续性和透明度.

背景情况:

  • 准确预测和解释企业ESG绿色洗对于透明度和监管有效性至关重要.
  • 现有的预测模型在超参数优化和可解释性方面存在局限性.
  • 需要先进的框架来应对ESG披露中的这些挑战.

研究的目的:

  • 开发和验证一个优化的机器学习框架,用于预测和解释企业的ESG环保行为.
  • 提高ESG绿色洗检测模型的准确性和可解释性.
  • 为监管机构和投资者提供可操作的见解.

主要方法:

  • 开发了一个全面的ESG绿色洗预测数据集.
  • 集成改进的猎人猎人优化 (IHPO) 用于超参数调整极端梯度提升 (XGBoost) 模型.
  • 为了模型的可解释性,利用了夏普利添加式解释 (SHAP).

主要成果:

  • 该IHPO-XGBoost模型在预测ESG绿色洗方面表现出卓越的表现 (R2=0.9790,RMSE=0.1376,MAE=0.1000).
  • 与传统的HPO-XGBoost和其他优化算法相比,实现了更高的准确性.
  • SHAP分析确定了影响预测结果的关键特征及其相互作用.

结论:

  • IHPO-XGBoost框架为预测和解释企业ESG绿色洗提供了一个强大的解决方案.
  • 增强的解释性为推动绿色洗行为的因素提供了关键的见解.
  • 调查结果支持在ESG背景下提高监管效率和明智的投资决策.