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Reliability and Validity
Prediction Intervals
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.
Distribution Reliability and Automation
Accuracy, limits, and approximation
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...
Sensitivity, Specificity, and Predicted Value
Sensitivity is the...
Receiver Operating Characteristic Plot
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可信性预测的适用性领域
1Chemometrics and Molecular Modeling Laboratory, Department of Chemistry & Physics, Kean University, Union, NJ, USA.
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.
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