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Reliability and Validity01:29

Reliability and Validity

12.7K
Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
12.7K
Prediction Intervals01:03

Prediction Intervals

2.2K
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. 
2.2K
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

107
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
107
Accuracy, limits, and approximation01:28

Accuracy, limits, and approximation

443
Accuracy, limits, and approximations are common in many fields, especially in engineering calculations. These concepts are imperative for ensuring that a given value is as close as possible to its true value.
Accuracy is defined as the closeness of the measured value to the true or actual value. In engineering mechanics, repeated measurements are taken during theoretical or experimental analyses to ensure that the result is precise and accurate.
The accuracy of any solution is based on the...
443
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

213
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...
213
Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

95
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
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相关实验视频

Updated: Jun 12, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.0K

可信性预测的适用性领域

Siyun Yang1, Supratik Kar2

  • 1Chemometrics and Molecular Modeling Laboratory, Department of Chemistry & Physics, Kean University, Union, NJ, USA.

Methods in molecular biology (Clifton, N.J.)
|September 23, 2024
PubMed
概括

了解适用性领域 (AD) 在人工智能 (AI) 和机器学习 (ML) 中至关重要. 本章详细介绍了AI/ML和定量结构-活动关系 (QSAR) 研究中的AD定义,方法和应用.

科学领域:

  • 人工智能 (AI) 是一种人工智能.
  • 机器学习 (ML) 是指机器学习.
  • 计算化学计算化学

背景情况:

  • 适用性领域 (AD) 的概念对于可靠的AI和ML模型解释至关重要.
  • 现有的文献主要集中在定量结构-活动关系 (QSAR) 建模中的AD.

研究的目的:

  • 在AI和ML的背景下提供AD的全面概述.
  • 探索各种方法和措施来定义和评估AD.
  • 突出AD在不同科学领域的多样化应用.

主要方法:

  • 定义和AD的理论基础.
  • 探索AD指标,包括DA指数 (κ, γ, δ),类概率估计,局部附近,提升,分类神经网络和子组发现 (SGD).
  • 对特定于QSAR建模的AD方法的审查.

主要成果:

  • 详细检查了AD在AI/ML中的作用和理论基础.
  • 确定AD评估的多种定量和定性方法.
  • 讨论AD在各个领域的广泛应用.

结论:

  • 充分了解AD对于人工智能和机器学习的知情研究和决策至关重要.
关键词:
适用性领域 适用性领域人工智能的人工智能是人工智能.机器学习是机器学习.在QSAR中使用QSAR.

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  • 讨论的方法提供了一个评估模型可靠性的框架.
  • AD的应用范围超越了QSAR,影响了更广泛的AI/ML实践.