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相关概念视频

Prediction Intervals01:03

Prediction Intervals

2.3K
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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Survival Tree01:19

Survival Tree

117
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...
117
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

388
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
388
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

507
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
507
Outliers and Influential Points01:08

Outliers and Influential Points

4.1K
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
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相关实验视频

Updated: Jul 26, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

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使用基于网络的隐私安全功能预测商家的未来业绩.

Mohsen Bahrami1, Hasan Alp Boz2, Yoshihiko Suhara3

  • 1MIT Connection Science, Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA. bahrami@mit.edu.

Scientific reports
|June 21, 2023
PubMed
概括
此摘要是机器生成的。

预测中小企业的业绩对于企业融资至关重要. 一种新方法使用信用卡交易网络,提供与传统方法相比的准确性,同时增强金融机构的数据隐私.

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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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科学领域:

  • 商业和经济学 商业和经济学
  • 计算机科学 计算机科学
  • 数据科学数据科学数据科学

背景情况:

  • 中小企业 (SMEs) 是重要的经济贡献者,需要对企业融资进行可靠的绩效预测.
  • 目前的中小企业业绩预测依赖于敏感的内部数据,对商家构成隐私风险.
  • 金融机构需要强大的方法来评估中小企业的信誉,而不影响机密信息.

研究的目的:

  • 开发一种保护隐私的方法来预测中小企业未来的业绩.
  • 为了利用信用卡交易数据进行商家绩效评估.
  • 提供商人和金融机构之间共享数据的安全替代方案.

主要方法:

  • 建立了一个商人网络,客户作为商人之间的中间人.
  • 提取的网络结构特征用于机器学习模型输入.
  • 与传统的收入和客户数据对比基于网络的特征的预测性能.

主要成果:

  • 使用网络衍生功能的机器学习模型实现了与使用传统财务指标的模型可比的预测性能.
  • 与依赖于直接收入或客户数据的方法相比,拟议的基于网络的方法显著提高了数据隐私.
  • 证明了使用匿名交易网络结构用于中小企业绩效评估的可行性.

结论:

  • 新型商家网络方法为中小企业业绩预测提供了隐私意识的解决方案.
  • 这种方法促进了更安全的数据共享,使金融机构能够做出明智的贷款决策.
  • 该研究解决了中小企业财务评估中的关键隐私问题,促进了经济增长.