Evaluating performance and determining optimum sample size for regression tree and automatic linear modeling

Genç, Serdar; Mendeş, Mehmet

doi:10.1590/1678-4162-12413

Cited by 8 publications

(5 citation statements)

References 8 publications

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“…Therefore, each estimate was made based on 100 trials. Then, the numbers of trials given below were determined (Genç and Mendeş, 2021b).…”

Section: Methodsmentioning

confidence: 99%

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Multiple Linear Regression versus Automatic Linear Modelling

Genç,

Mendeş

2024

Arq. Bras. Med. Vet. Zootec.

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In this study, performances of Multiple Linear Regression and Automatic Linear Modelling are compared for different sample sizes and number of predictors. A comprehensive Monte Carlo simulation study was carried out for this purpose. Random numbers generated from multivariate normal distribution by using RNMVN function of IMSL library of Microsoft FORTRAN Developer Studio composed the material of this study. Results of the simulation study showed that the sample size and the number of predictors are the main factors that lead to produce different results. Although both methods gave very similar results especially when studied with large sample sizes (n≥100), the Automatic linear modelling is preferred for analyzing data sets due to its simplicity in analyzing data and interpreting the results, ability to present results visually and providing more detailed information especially studying large complex data sets. It will be beneficial to use the Automatic linear modelling especially in analyzing massive and complex data sets for the purposes of investigating the relationships between one continuous dependent and 10 or more predictors and determine the factors that affect the response or target variable. At the same time, it will also be possible to evaluate the effect of each predictor with a more detailed response.

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“…Therefore, each estimate was made based on 100 trials. Then, the numbers of trials given below were determined (Genç and Mendeş, 2021b).…”

Section: Methodsmentioning

confidence: 99%

“…It is because the ALM includes automatic data preparation steps. Therefore, after the final candidate models are determined, it is of great benefit to carefully evaluate these models by considering various criteria and asking some important questions (IBM..., 2012; Yang, 2013;Genç and Mendeş, 2021b;Mendeş, 2021).…”

Section: Methodsmentioning

confidence: 99%

Multiple Linear Regression versus Automatic Linear Modelling

Genç,

Mendeş

2024

Arq. Bras. Med. Vet. Zootec.

Self Cite

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“…Given the limitations of the traditional regression procedure, a decision was made to use the automatic linear modelling procedure, which has been included in the IBM SPSS 27v package for linear modelling and speeds up the process of data analysis through several automatic mechanisms [43][44][45][46].…”

Section: Data Simulation Methods Using Automatic Linear Modellingmentioning

confidence: 99%

A Study of the Relationship between Lithuanian International Migration Flows and Transport Sector Performance Indicators

Ševčenko-Kozlovska

Čižiūnienė

2022

Sustainability

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Migration, emigration and immigration are processes directly related to transport and have a significant impact on the performance of this field. On the other hand, extensive movement of people inevitably relates to issues of sustainability, their assurance, etc. Migrants settle in large cities where public transport is well developed. Migrants usually commute by means other than driving their own cars (choosing public transport, walking, cycling or car sharing). Many researchers in the USA (United States of America) have linked this to migrants’ choice of a place of residence, which are well served by public transport, as they mainly travel for work. However, with passing time and improving living standards, the need for immigrants to not only use public transport, but to also own their own, becomes similar to that of the locals. This also increases the need for the analysis of sustainable transport issues. The correlation and regression analyses used in the article and the application of the ALM (automatic linear modelling) modelling led to the following results: a significant positive correlation was found between emigration and passenger carriage by all modes of transport|Maritime transport, and a significant negative correlation was found between emigration and passenger carriage by all modes of transport|Rail transport, suggesting that these indicators have a mutually significant relationship with one another. The conducted regression analysis and applied modelling showed that the regression model was insignificant and therefore cannot be used. However, the results suggest that the relationship between immigration and transport indicators can be used as a direction for further research.

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“…For both comparisons, these only slightly varying results can be related to the small number of covariates. The values of p and n influence the performance of the machine learning models, as noted by [36], who studied the performance of the models based on trees according to the sizes of these quantities.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

New Partially Linear Regression and Machine Learning Models Applied to Agronomic Data

Rodrigues,

Ortega,

Cordeiro

2023

Axioms

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Regression analysis can be appropriate to describe a nonlinear relationship between the response variable and the explanatory variables. This article describes the construction of a partially linear regression model with two systematic components based on the exponentiated odd log-logistic normal distribution. The parameters are estimated by the penalized maximum likelihood method. Simulations for some parameter settings and sample sizes empirically prove the accuracy of the estimators. The superiority of the proposed regression model over other regression models is shown by means of agronomic experimentation data. The predictive performance of the new model is compared with two machine learning techniques: decision trees and random forests. These methods achieved similar prediction performance, i.e., none stands out as a better predictor. In this sense, the objective of the research is to choose the best method. If the objective is only predictive, the decision tree can be used due to its simplicity. For inference purposes, the regression model is recommended, which can provide much more information regarding the relationship of the variables under study.

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Evaluating performance and determining optimum sample size for regression tree and automatic linear modeling

Cited by 8 publications

References 8 publications

Multiple Linear Regression versus Automatic Linear Modelling

Multiple Linear Regression versus Automatic Linear Modelling

A Study of the Relationship between Lithuanian International Migration Flows and Transport Sector Performance Indicators

New Partially Linear Regression and Machine Learning Models Applied to Agronomic Data

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