Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

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 Toward the Mean01:52

Regression Toward the Mean

6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Multiple Regression01:25

Multiple Regression

2.9K
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...
2.9K
Variation01:19

Variation

6.7K
An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
6.7K
Mean Absolute Deviation01:13

Mean Absolute Deviation

2.6K
The mean absolute deviation is also a measure of the variability of data in a sample. It is the absolute value of the average difference between the data values and the mean.
Let us consider a dataset containing the number of unsold cupcakes in five shops: 10, 15, 8, 7, and 10. Initially, calculate the sample mean. Then calculate the deviation, or the difference, between each data value and the mean. Next, the absolute values of these deviations are added and divided by the sample size to...
2.6K
Regression Analysis01:11

Regression Analysis

5.5K
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:
5.5K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Smoking behavior detection algorithm based on YOLOv8-MNC.

Frontiers in computational neuroscience·2023
Same author

YOLOv7-CSAW for maritime target detection.

Frontiers in neurorobotics·2023
Same author

Face Mask-Wearing Detection Model Based on Loss Function and Attention Mechanism.

Computational intelligence and neuroscience·2022
Same author

Time-varying feature selection for longitudinal analysis.

Statistics in medicine·2019
Same author

Fabrication and Optimization of Self-Microemulsions to Improve the Oral Bioavailability of Total Flavones of Hippophaë rhamnoides L.

Journal of food science·2017
Same author

Expanded alleles of the <i>FMR1</i> gene are related to unexplained recurrent miscarriages.

Bioscience reports·2017
Same journal

Turbulent flow in a vortex separator with a directed pipe inlet.

Scientific reports·2026
Same journal

Systematic characteristic evaluation of clay-based cementitious material derived from calcium carbide residue and waste tile powder.

Scientific reports·2026
Same journal

Retraction Note: Improvement of a rapid diagnostic application of monoclonal antibodies against avian influenza H7 subtype virus using Europium nanoparticles.

Scientific reports·2026
Same journal

Applying large language models to spam detection in the Kazakh low-resource language setting.

Scientific reports·2026
Same journal

An open-source 3D printing system enabling in-situ freeze-thaw processing of hydrogels.

Scientific reports·2026
Same journal

An enhanced EfficientNet framework for automated waste classification using cosine annealing and label smoothing.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: May 26, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K

Within-project and cross-project defect prediction based on model averaging.

Tong Li1, Zhong Wang2, Peibei Shi1

  • 1School of Computer and Artificial Intelligence, Hefei Normal University, No. 1688 Jinxiu Avenue, Hefei, 230601, Anhui, China.

Scientific Reports
|February 21, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a model averaging technique for software defect prediction. This method improves accuracy in both within-project and cross-project defect detection compared to existing algorithms.

More Related Videos

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

6.6K
Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

653

Related Experiment Videos

Last Updated: May 26, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K
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

6.6K
Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

653

Area of Science:

  • Computer Science
  • Software Engineering

Background:

  • Software defect prediction is crucial for economic and financial sectors.
  • Early identification of defective software modules is highly significant.

Purpose of the Study:

  • To propose a novel within-project and cross-project software defect prediction technology.
  • To enhance prediction performance using model averaging theory.

Main Methods:

  • Utilized XGBoost and LightGBM as candidate machine learning models.
  • Applied model averaging by determining weights to minimize prediction errors.
  • Evaluated performance using cross-validation on four public datasets (NASA, AEEEM, ReLink, SoftLab).

Main Results:

  • Model averaging showed slightly improved results over XGBoost and LightGBM for within-project prediction.
  • Outperformed seven traditional machine learning algorithms in most within-project scenarios.
  • Demonstrated overall superior performance compared to four benchmark methods in cross-project prediction.

Conclusions:

  • The proposed model averaging method achieves robust and accurate software defect prediction.
  • This approach is effective for both within-project and cross-project defect prediction tasks.