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相关概念视频

Confidence Coefficient01:24

Confidence Coefficient

7.5K
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...
7.5K
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

5.6K
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...
5.6K
Ordinal Level of Measurement00:55

Ordinal Level of Measurement

22.9K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks...
22.9K
Confidence Intervals01:21

Confidence Intervals

6.1K
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...
6.1K
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
Regression Analysis01:11

Regression Analysis

5.6K
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:
5.6K

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相关实验视频

Updated: Jun 5, 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

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对于回归模型预测的顺序信心水平分配.

Steven Kearnes1, Patrick Riley1

  • 1Relay Therapeutics, Cambridge, Massachusetts 02142, United States.

Journal of chemical information and modeling
|December 9, 2024
PubMed
概括
此摘要是机器生成的。

我们开发了一种简单的方法,通过回归模型对分子性质预测赋予可靠的信心分数. 这种方法通过提供可解释的信任级别来帮助药物发现的决策.

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相关实验视频

Last Updated: Jun 5, 2025

An R-Based Landscape Validation of a Competing Risk Model
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An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

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Assessment and Communication for People with Disorders of Consciousness
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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科学领域:

  • 计算化学是一种计算化学.
  • 机器学习在药物发现中的作用
  • 量化结构与属性关系 (QSPR) 建模

背景情况:

  • 准确预测分子性质对于有效的药物发现至关重要.
  • 评估这些预测的可靠性对于明智的决策至关重要.
  • 现有的方法可能缺乏解释性或简单的应用.

研究的目的:

  • 引入一种简单可解释的方法来量化预测信心.
  • 为了在药物发现管道中实现更好的决策.
  • 为了验证拟议的信任分配方法.

主要方法:

  • 为回归模型开发一种新的信心评分技术.
  • 时间分割验证的应用,以进行可靠的绩效评估.
  • 利用来自Relay Therapeutics的内部测试数据进行实证评估.

主要成果:

  • 拟议的方法成功地为分子性质预测赋予了准确和可解释的信心水平.
  • 在现实的药物发现环境中使用时间分割验证证明有效性.
  • 自信水平证明是指导药物发现计划决策的宝贵指标.

结论:

  • 提出的方法为提高分子性质预测可靠性的实际解决方案.
  • 改进的信心评估促进了更有效和数据驱动的药物发现策略.
  • 这种方法有可能加速候选药物的识别和优化.