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相关概念视频

Decision Making: P-value Method01:09

Decision Making: P-value Method

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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
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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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Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
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Factorial Design02:01

Factorial Design

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Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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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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相关实验视频

Updated: May 29, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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一个基于XGBoost算法和受约束参数方法的多维决策框架.

Xinxin Wang1, BingBing Zhang1, Zeshui Xu1

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

Scientific reports
|February 5, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的决策框架,使用嵌套的概率语言术语集和XGBoost算法来进行不确定的决策. 它有效地整合了定量数据和定性变量,以导出属性权重并创建用于实际应用的科学决策矩阵.

关键词:
银行信贷银行信贷有限制的参数方法.多维决定的多维决定.嵌套的概率语言术语集 嵌套的概率语言术语集在XGBoost算法中,我们使用了XGBoost算法.

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相关实验视频

Last Updated: May 29, 2025

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科学领域:

  • 决策科学 决策科学 决策科学
  • 数据分析数据分析数据分析.
  • 机器学习 机器学习

背景情况:

  • 由于数据的复杂性和不确定性,组织在决策方面面临挑战.
  • 对可解释和数据驱动的分析方法的需求正在增加.
  • 现有的方法可能无法充分处理定量数据和定性语言变量.

研究的目的:

  • 提出一个多维的决策框架,整合定量数据和定性变量.
  • 开发一种使用历史数据和机器学习计算属性权重的方法.
  • 为在不确定的环境中合理决策提供科学决策矩阵.

主要方法:

  • 利用嵌套的概率语言术语集用于决策建模.
  • 应用了XGBoost算法来从历史数据中确定属性重要性和权重.
  • 采用受约束的参数方法来确定语言变量的成员函数.

主要成果:

  • 开发了一个灵活而稳定的决策框架.
  • 通过整合数据和语言变量成功生成了科学决策矩阵.
  • 通过银行信用评级案例研究证明了框架的有效性.

结论:

  • 拟议的框架为捕获,整合和分析数据和语言变量提供了一个强大的方法.
  • 它支持管理者和决策者确定属性权重和选择适当的决策方法.
  • 该框架增强了不确定性和认知决策的理论理解和实际应用.