Jove
Visualize
联系我们

相关概念视频

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
Microsoft Excel: Regression Analysis01:18

Microsoft Excel: Regression Analysis

536
Regression analysis in Microsoft Excel is a powerful statistical method for examining the relationship between a dependent variable and one or more independent variables. It's used extensively in fields such as economics, biology, and business to predict outcomes, understand relationships, and make data-driven decisions. The most common type is linear regression, which attempts to fit a straight line through the data points to model the relationship between variables.
To perform regression...
536
Aggregates Classification01:29

Aggregates Classification

310
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
310
Regression Analysis01:11

Regression Analysis

5.7K
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:
5.7K
Stereotype Content Model02:16

Stereotype Content Model

14.0K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
14.0K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

300
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
300

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

External validation of GDM risk prediction models using a machine learning reciprocal model-exchange framework.

Computers in biology and medicine·2026
Same author

Label Accuracy in Electronic Health Records and Its Impact on Machine Learning Models for Early Prediction of Gestational Diabetes: 3-Step Retrospective Validation Study.

JMIR medical informatics·2025
Same author

An investigation of pre-stimulus eeg for prediction of driver reaction time.

Biomedical physics & engineering express·2025
Same author

DERCo: A Dataset for Human Behaviour in Reading Comprehension Using EEG.

Scientific data·2024
Same author

Lack of Data Sharing Despite Data Availability Statements in Studies Using Machine Learning Models for Prediction of Gestational Diabetes Mellitus.

Diabetes care·2024
Same author

From lab to life: assessing the impact of real-world interactions on the operation of rapid serial visual presentation-based brain-computer interfaces.

Journal of neural engineering·2024
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

相关实验视频

Updated: Jun 15, 2025

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.6K

在多个产品类别中使用机器学习预测未来的客户需求.

David Kilroy1, Graham Healy2, Simon Caton1

  • 1School of Computer Science, University College Dublin, Dublin, Ireland.

PloS one
|August 26, 2024
PubMed
概括

本研究介绍了一种模型,用于预测未来从在线内容中预测流行的产品需求. 它准确地预测新兴的客户需求,为企业提供早期市场准入.

科学领域:

  • 计算语言学 计算语言学
  • 市场研究市场研究
  • 数据科学数据科学数据科学

背景情况:

  • 从用户生成的内容中提取客户需求的现有方法往往忽略了未来的产品趋势.
  • 对即将出现的流行产品的未满足需求的识别仍然是市场分析中的一个重大挑战.

研究的目的:

  • 开发一个监督的关键短语分类模型来预测未来的流行产品需求.
  • 利用趋势客户需求 (TCN) 数据集用于训练预测算法.

主要方法:

  • 利用趋势客户需求 (TCN) 数据集 (2011-2021) 涵盖各种消费包装商品.
  • 采用在Reddit关键词功能上训练的时间序列算法来预测未来的需求 (未来1-3年).
  • 实施多任务学习,以实现跨类别的预测.

主要成果:

  • 拟议的模型的性能优于现有的文献基线.
  • 准确预测新出现的需求,即使是不包括在培训数据中的产品类别 (例如,预测牙膏,谷物和酒培训后的洗发水需求).

结论:

  • 开发的模型有效地从在线数据中预测未来的流行客户需求.
  • 这种方法提供了重要的商业优势,包括早期进入市场和竞争洞察力.

更多相关视频

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K

相关实验视频

Last Updated: Jun 15, 2025

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.6K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K