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相关概念视频

Survival Tree01:19

Survival Tree

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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...
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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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Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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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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Regression Toward the Mean01:52

Regression Toward the Mean

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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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Goodness-of-Fit Test01:16

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The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...
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相关实验视频

Updated: Jul 9, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

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个人违约风险的预测基于一个子搜索算法与支持矢量机器模型的算法.

Xu Shen1,2, Xinyu Wang1

  • 1School of Economics and Management, China University of Mining and Technology, Xuzhou 221116, China.

Mathematical biosciences and engineering : MBE
|December 5, 2023
PubMed
概括

本研究介绍了一种混合机器学习模型,即Sparrow Search Algorithm-Support Vector Machine (SSA-SVM),用于预测个人信用违约风险. 与传统的支持矢量机器相比,SSA-SVM模型表现出卓越的性能.

关键词:
在SSA-SVM模型中,商业银行是商业银行.信用评价 信用评价 信用评价违约风险 违约风险 违约风险预测的准确性 预测的准确性

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科学领域:

  • 机器学习 机器学习
  • 金融风险管理 金融风险管理
  • 计算智能是一种计算智能.

背景情况:

  • 准确的个人信用评价对于商业银行来说至关重要,以减轻财务损失.
  • 传统的信用评分模型经常与复杂,高维数据集作斗争.
  • 机器学习为提高违约风险预测准确度提供了有希望的途径.

研究的目的:

  • 开发和评估一种新的混合模型,用于预测个人信用违约风险.
  • 为了研究将Sparrow搜索算法 (SSA) 与支持矢量机器 (SVM) 结合起来,实现这一任务的有效性.
  • 为了证明在商业银行业务中提出的SSA-SVM模型的实际价值.

主要方法:

  • 用个人信用数据进行分析.
  • 应用统计分析,数据规范化和数据预处理的主要因素分析.
  • 开发并实施混合SSA-SVM模型用于违约风险预测.
  • 将SSA-SVM模型的性能与标准SVM模型进行比较.

主要成果:

  • 与原始数据相比,数据预处理技术显著改善了评估指数的表现.
  • 在各种评估指标上,SSA-SVM模型的表现始终优于标准的SVM模型.
  • 混合模型在预测个人违约风险方面表现出更高的准确性.

结论:

  • 建议的数据处理方法对于个人信用数据是有效的.
  • 在个人违约风险预测方面,SSA-SVM混合模型对SVM模型提供了显著的改进.
  • 对于商业银行来说,SSA-SVM模型具有相当大的实际价值,它们希望改进其信贷评估流程.