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相关概念视频

Confidence Coefficient01:24

Confidence Coefficient

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The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
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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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Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

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A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
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Confidence Intervals01:21

Confidence Intervals

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An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
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Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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数据策划和信心水平对使用机器学习模型的复合预测的影响.

Elena Xerxa1,2, Martin Vogt1,2, Jürgen Bajorath1,2,3

  • 1B-IT, Department of Life Science Informatics and Data Science, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, Bonn D-53115, Germany.

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概括

数据策划显著提高了机器学习 (ML) 模型在化学中的性能. 对化学数据进行顺序处理,通过完善数据质量和化学空间分离,逐步提高分类准确性.

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科学领域:

  • 化学 化学 化学
  • 数据科学数据科学数据科学
  • 机器学习 机器学习

背景情况:

  • 数据策划在数据科学中至关重要,但在化学机器学习中经常被忽视.
  • 评估数据策划对分子机器学习 (ML) 模型的影响至关重要.

研究的目的:

  • 评估数据策划对分子ML模型性能的影响.
  • 开发和评估化合物和活性数据的顺序治理方案.

主要方法:

  • 为化学化合物和活性数据开发了一种连续的固化方案.
  • 机器学习分类模型是在不断增加的数据置信度水平时生成的.
  • 在不同的数据策划级别中评估了模型性能.

主要成果:

  • 通过顺序的数据策划,观察到分类性能有系统和渐进的增加.
  • 数据处理增强了化学空间中具有不同类别标签的化合物的分离.
  • 取消单元,而不是模拟序列,主要推动了化学空间分离的改善.

结论:

  • 严格的数据策划直接导致化学应用中ML模型的性能提高.
  • 在开发和评估化学ML模型时,应仔细考虑不同的数据处理和置信级别.