Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Correlation and Regression00:53

Correlation and Regression

1.3K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
1.3K
Multiple Regression01:25

Multiple Regression

3.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.0K
Coefficient of Correlation01:12

Coefficient of Correlation

6.2K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
6.2K
Frequency-dependent Selection01:21

Frequency-dependent Selection

22.1K
When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
22.1K
Spearman's Rank Correlation Test01:20

Spearman's Rank Correlation Test

840
Spearman's rank correlation test, also known as Spearman's rho, is a nonparametric method for assessing the strength and direction of association between two variables. This test is particularly valuable when the data distribution is unknown or when the assumption of normality does not hold. Named after the English psychologist and statistician Dr. Charles Edward Spearman, it serves as the nonparametric counterpart to Pearson's correlation coefficient.
Spearman's test calculates...
840
Random Sampling Method01:09

Random Sampling Method

11.2K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
11.2K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Hydrophilic Janus Micelles From an ABC Triblock Copolymer.

Angewandte Chemie (International ed. in English)·2026
Same author

Transferrin receptor 1-targeted polymersomes therapy for colorectal cancer.

Materials today. Bio·2025
Same author

Membrane interactions of Ocellatins. Where do antimicrobial gaps stem from?

Amino acids·2021
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

Updated: Jul 15, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.5K

在随机森林中基于距离相关的特征选择.

Suthakaran Ratnasingam1, Jose Muñoz-Lopez1

  • 1Department of Mathematics, California State University, San Bernardino, CA 92407, USA.

Entropy (Basel, Switzerland)
|September 28, 2023
PubMed
概括
此摘要是机器生成的。

距离相关性为皮尔森相关性提供了一个强大的替代方案,用于检测所有依赖关系,而不仅仅是线性依赖关系. 我们使用距离相关的新过方法在高维,非线性数据的随机森林回归中表现出色.

关键词:
皮尔森相关性 皮尔森相关性距离相关性 距离相关性功能选择 功能选择随机的森林随机的森林

更多相关视频

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.6K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

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

Published on: March 1, 2024

798

相关实验视频

Last Updated: Jul 15, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.5K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.6K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

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

Published on: March 1, 2024

798

科学领域:

  • 统计 统计 统计 统计
  • 机器学习 机器学习
  • 数据挖掘 数据挖掘

背景情况:

  • 皮尔森相关系数 (ρ) 仅限于变量之间的线性关系.
  • 现有的特征选择方法可能无法捕捉复杂的非线性依赖关系.
  • 需要的方法可以识别所有类型的可变依赖关系.

研究的目的:

  • 提出一种新的选方法,用于随机森林回归中的特征选择.
  • 使用距离相关性作为识别相关特征的标准.
  • 评估拟议方法的性能与现有技术相比.

主要方法:

  • 实施基于距离相关性的过方法,用于特征选择.
  • 使用随机森林回归作为预测模型.
  • 在各种数据设置中进行广泛的模拟研究.
  • 将预测平均平方误差 (MSE) 与现有方法进行比较.

主要成果:

  • 拟议的距离相关性过方法与现有方法具有竞争力.
  • 该方法在具有非线性关系的高维 (p≥300) 数据集中显著优于其他技术.
  • 该方法通过现实世界的数据示例来证明其实际适用性.

结论:

  • 距离相关性是功能选择的强大工具,捕捉非线性依赖.
  • 拟议的过方法提高了随机森林回归性能,特别是在高维,复杂的数据集中.
  • 这种方法为统计建模和机器学习应用提供了宝贵的进步.