Incidence in the forecast by applying variable reduction. A practical example
DOI:
https://doi.org/10.36825/RITI.08.15.006Keywords:
Variable Reduction, Factorial Analysis, Classification Methods, Neural NetworksAbstract
This paper shows how the forecast affects reducing the number of variables with which the prediction is made, for this the factor analysis was applied. The variables were grouped into three factors. The following classification methods were used: discriminant analysis, logistic regression and neural networks. We worked with three groups of data, the first includes the original variables, the second the variables belonging to factors one, two and three and the third is composed only of those of factor one. Confusion matrices and ROC curves were used to determine the accuracy of the forecast models. The results obtained for each group are shown, where it is appreciated that the reduction of variables is very convenient to reach excellent prediction results using fewer resources; An example of this is the case of logistic regression where the difference in the accuracy of the model between the first two groups is less than three percent.
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