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 7 publications

(2 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%