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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

376
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
376

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

Global Functioning and Mental Health Parameters: Examining Interplay and Improvements in Inpatient Psychiatry.

International journal of methods in psychiatric research·2026
Same author

A practice-oriented guide to statistical inference in linear modeling for non-normal or heteroskedastic error distributions.

Behavior research methods·2025
Same author

Automatic- and Transformer-Based Automatic Item Generation: A Critical Review.

Journal of Intelligence·2025
Same author

It's a Challenge, Not a Threat: Lecturers' Satisfaction During the Covid-19 Summer Semester of 2020.

Frontiers in psychology·2021
Same author

Effects of alpha and gamma transcranial alternating current stimulation (tACS) on verbal creativity and intelligence test performance.

Neuropsychologia·2017
Same author

Affective Evaluation of One's Own and Others' Body Odor: The Role of Disgust Proneness.

Perception·2017

相关实验视频

Updated: Mar 10, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.9K

在心理学研究中比较机器学习和人工神经网络模型:基于ROC的分析.

Marie-Luise Leitner1, Martin Arendasy1

  • 1Department of Psychology, University of Graz, Graz, Austria.

Frontiers in psychology
|March 9, 2026
PubMed
概括

传统的机器学习模型,如逻辑回归和随机森林,在心理选择任务中优于人工神经网络. 这些古典方法在应用评估环境中提供了更稳定,更易于解释的结果.

科学领域:

  • 心理评估 心理评估
  • 机器学习 机器学习
  • 人工智能的人工智能

背景情况:

  • 数据驱动的方法越来越多地用于心理评估.
  • 人工神经网络与传统机器学习模型在选择环境中的比较性能尚未得到充分证实.
  • 使用现实世界的心理数据集和基于ROC的评估存在有限的比较证据.

研究的目的:

  • 在应用心理选择中,比较传统机器学习模型与人工神经网络的有效性.
  • 在真实数据集上使用准确度和接收器操作特征 (ROC) 分析来评估模型性能.

主要方法:

  • 将后勤回归,决策树和随机森林模型与前人工神经网络进行比较.
  • 使用了4,155名大学入学考试申请人的数据集.
  • 评估模型使用ROC分析的精度和曲线下的面积 (AUC).

主要成果:

  • 后勤回归产生了最高的预测性能 (精度=0.973,AUC=0.99),紧随其后的是随机森林 (精度=0.961,AUC=0.98).
  • 人工神经网络实现了较低的辨别能力 (AUC = 0.87),并显示出过度配合的迹象.
  • 生物学,化学和数理推理被确定为关键预测因素.
关键词:
ROC (接收器操作特征)人工神经网络的人工神经网络决策树是一个决策树.重要的特征 重要的特征 重要的特征逻辑回归的逻辑回归机器学习是机器学习.噪音 噪音 噪音 噪音过度适应 过度适应

更多相关视频

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.6K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

839

相关实验视频

Last Updated: Mar 10, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.9K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.6K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

839

结论:

  • 与浅层神经网络相比,传统的机器学习模型为中型,结构化的心理数据集提供了更稳定,更易解释和更强大的性能.
  • 模型选择和诱导偏见在应用心理学研究中至关重要.
  • 经典机器学习方法在选择和评估环境中仍然很有价值.