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相关概念视频

Bootstrapping01:24

Bootstrapping

628
The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
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Survival Tree01:19

Survival Tree

105
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
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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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Random Sampling Method01:09

Random Sampling Method

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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...
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Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure 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.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
12.0K
Stratified Sampling Method01:16

Stratified Sampling Method

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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. 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.
To choose a stratified sample, divide the population into groups called strata and then take a...
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相关实验视频

Updated: Jul 17, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

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基于随机森林的建筑成本预测系统,由鸟算法优化.

Zhishan Zheng1, Lin Zhou2, Han Wu3

  • 1School of Big data and Computer, Jiangxi University of Engineering, Xinyu 338000, China.

Mathematical biosciences and engineering : MBE
|September 7, 2023
PubMed
概括
此摘要是机器生成的。

精确的施工成本预测是使用一种新的随机森林模型优化了鸟算法改进. 这种方法提高了复杂项目的预测准确性和效率.

关键词:
鸟算法 鸟算法随机的森林 随机的森林建筑工程是建筑工程的工程.建设成本预测 预测

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科学领域:

  • 建设管理建设管理.
  • 预测建模预测建模
  • 在工程领域的人工智能.

背景情况:

  • 建筑成本预测面临诸多挑战,包括精度低,效率差以及由于项目的复杂性而造成的高度不确定性.
  • 现有的方法经常与建筑成本因素的动态和多方面的性质作斗争.

研究的目的:

  • 开发一个先进的预测指数系统和一个强大的预测模型,用于建设成本.
  • 通过解决传统方法的局限性,提高建筑成本预测的准确性和效率.

主要方法:

  • 确定影响建筑成本的关键因素,并开发一个14指数预测系统.
  • 构建一个随机森林 (RF) 预测模型,使用鸟算法 (BSA) 为参数调整进行优化.
  • 使用来自中国建筑公司的工程数据进行验证,将BSA与其他优化算法和RF与其他预测方法进行比较.

主要成果:

  • 拟议模型的最大相对误差仅为1.24%,符合工程实践要求.
  • 与其他元启发算法相比,鸟算法证明了与最佳参数的融合速度更快.
  • 在准确性和效率方面,RF-BSA模型超过了传统和先进的预测方法.

结论:

  • 开发的预测模型为建筑成本预测提供了显著的改进.
  • 通过更可靠的预测,这些发现为优化建筑项目的成本管理提供了基础.