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相关概念视频

Associative Learning01:27

Associative Learning

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
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Prediction Intervals01:03

Prediction Intervals

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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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Multiple Regression01:25

Multiple Regression

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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...
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Regression Analysis01:11

Regression Analysis

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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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Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

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Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
412
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

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The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
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相关实验视频

Updated: May 27, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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客户信用评分的依赖性成本敏感学习基于选择性深层组合模型的例子.

Jin Xiao1, Sihan Li2, Yuhang Tian1

  • 1Business School, Sichuan University, Chengdu, 610064, China.

Scientific reports
|February 18, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的信用评分模型 (ECS-SDE),该模型通过考虑每个例子的不同成本来处理不平衡的数据. 该模型提高了信用评分任务的性能和可解释性.

关键词:
信用评分是指信用评分.依赖于实例的成本敏感学习.可解释的人工智能选择性的深层合奏.TabNet是一个深度神经网络.

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科学领域:

  • 机器学习 机器学习
  • 数据科学数据科学数据科学
  • 金融分析 金融分析

背景情况:

  • 分类不平衡的数据是信用评分的一个常见挑战.
  • 传统的成本敏感方法往往无法解释不同的样本成本,并且缺乏现实世界的适用性.
  • 现有模型的有限解释性阻碍了在财务决策中实际采用.

研究的目的:

  • 为增强客户信用评分提出一种新的以实例为依赖的基于成本敏感学习的选择性深层组合 (ECS-SDE) 模型.
  • 通过整合不同的成本和提高模型解释性来解决传统方法的局限性.
  • 开发一种信用评分解决方案,更好地与业务需求保持一致,并提供透明的决策.

主要方法:

  • 开发了一种ECS-SDE模型,将依赖实例的成本敏感学习与TabNet (专注可解释的表式学习) 和GMDH (数据处理的组方法) 深度神经网络集成在一起.
  • 使用TabNet作为基础分类器,使用一个依赖于示例的成本损失函数优化它对不平衡的数据.
  • 设计了一个GMDH,具有依赖于示例的成本敏感对称标准,用于选择性深度集成基础分类器,减少冗余并提高性能.

主要成果:

  • 在信用评分方面,ECS-SDE模型表现出优越的整体性能,与六个成本敏感和五个先进的深层合并模型相比.
  • 在四个数据集中实现了BS+,Save和AUC指标的显著优势.
  • 该模型提供了强大的解释性,详细分析突出了信用评分中的关键特征角色.

结论:

  • 拟议的ECS-SDE模型有效地解决了信用评分数据中的类不平衡和变化成本.
  • 与现有方法相比,ECS-SDE提供了更好的分类性能和更好的解释性.
  • 这种方法为实际的信用评分应用提供了更强大,更透明的解决方案.